diff --git a/datafusion/core/tests/physical_optimizer/projection_pushdown.rs b/datafusion/core/tests/physical_optimizer/projection_pushdown.rs
index 95cd34b6e4553..6018d714c5955 100644
--- a/datafusion/core/tests/physical_optimizer/projection_pushdown.rs
+++ b/datafusion/core/tests/physical_optimizer/projection_pushdown.rs
@@ -46,7 +46,7 @@ use datafusion_physical_optimizer::output_requirements::OutputRequirementExec;
 use datafusion_physical_optimizer::projection_pushdown::ProjectionPushdown;
 use datafusion_physical_plan::coalesce_partitions::CoalescePartitionsExec;
 use datafusion_physical_plan::coop::CooperativeExec;
-use datafusion_physical_plan::filter::FilterExec;
+use datafusion_physical_plan::filter::{FilterExec, FilterExecBuilder};
 use datafusion_physical_plan::joins::utils::{ColumnIndex, JoinFilter};
 use datafusion_physical_plan::joins::{
     HashJoinExec, NestedLoopJoinExec, PartitionMode, StreamJoinPartitionMode,
@@ -1754,3 +1754,121 @@ fn test_hash_join_empty_projection_embeds() -> Result<()> {
 
     Ok(())
 }
+
+/// Regression test for <https://github.com/apache/datafusion/issues/21459>
+///
+/// When a `ProjectionExec` sits on top of a `FilterExec` that already carries
+/// an embedded projection, the `ProjectionPushdown` optimizer must not panic.
+///
+/// Before the fix, `FilterExecBuilder::from(self)` copied stale projection
+/// indices (e.g. `[0, 1, 2]`). After swapping, the new input was narrower
+/// (2 columns), so `.build()` panicked with "project index out of bounds".
+#[test]
+fn test_filter_with_embedded_projection_after_projection() -> Result<()> {
+    // DataSourceExec: [a, b, c, d, e]
+    let csv = create_simple_csv_exec();
+
+    // FilterExec: a > 0, projection=[0, 1, 2] → output: [a, b, c]
+    let predicate = Arc::new(BinaryExpr::new(
+        Arc::new(Column::new("a", 0)),
+        Operator::Gt,
+        Arc::new(Literal::new(ScalarValue::Int32(Some(0)))),
+    ));
+    let filter: Arc<dyn ExecutionPlan> = Arc::new(
+        FilterExecBuilder::new(predicate, csv)
+            .apply_projection(Some(vec![0, 1, 2]))?
+            .build()?,
+    );
+
+    // ProjectionExec: narrows [a, b, c] → [a, b]
+    let projection: Arc<dyn ExecutionPlan> = Arc::new(ProjectionExec::try_new(
+        vec![
+            ProjectionExpr::new(Arc::new(Column::new("a", 0)), "a"),
+            ProjectionExpr::new(Arc::new(Column::new("b", 1)), "b"),
+        ],
+        filter,
+    )?);
+
+    let initial = displayable(projection.as_ref()).indent(true).to_string();
+    let actual = initial.trim();
+    assert_snapshot!(
+        actual,
+        @r"
+    ProjectionExec: expr=[a@0 as a, b@1 as b]
+      FilterExec: a@0 > 0, projection=[a@0, b@1, c@2]
+        DataSourceExec: file_groups={1 group: [[x]]}, projection=[a, b, c, d, e], file_type=csv, has_header=false
+    "
+    );
+
+    // This must not panic
+    let after_optimize =
+        ProjectionPushdown::new().optimize(projection, &ConfigOptions::new())?;
+    let after_optimize_string = displayable(after_optimize.as_ref())
+        .indent(true)
+        .to_string();
+    let actual = after_optimize_string.trim();
+    assert_snapshot!(
+        actual,
+        @r"
+    FilterExec: a@0 > 0
+      DataSourceExec: file_groups={1 group: [[x]]}, projection=[a, b], file_type=csv, has_header=false
+    "
+    );
+
+    Ok(())
+}
+
+/// Same as above, but the outer ProjectionExec also renames columns.
+/// Ensures the rename is preserved after the projection pushdown swap.
+#[test]
+fn test_filter_with_embedded_projection_after_renaming_projection() -> Result<()> {
+    let csv = create_simple_csv_exec();
+
+    // FilterExec: b > 10, projection=[0, 1, 2, 3] → output: [a, b, c, d]
+    let predicate = Arc::new(BinaryExpr::new(
+        Arc::new(Column::new("b", 1)),
+        Operator::Gt,
+        Arc::new(Literal::new(ScalarValue::Int32(Some(10)))),
+    ));
+    let filter: Arc<dyn ExecutionPlan> = Arc::new(
+        FilterExecBuilder::new(predicate, csv)
+            .apply_projection(Some(vec![0, 1, 2, 3]))?
+            .build()?,
+    );
+
+    // ProjectionExec: [a as x, b as y] — narrows and renames
+    let projection: Arc<dyn ExecutionPlan> = Arc::new(ProjectionExec::try_new(
+        vec![
+            ProjectionExpr::new(Arc::new(Column::new("a", 0)), "x"),
+            ProjectionExpr::new(Arc::new(Column::new("b", 1)), "y"),
+        ],
+        filter,
+    )?);
+
+    let initial = displayable(projection.as_ref()).indent(true).to_string();
+    let actual = initial.trim();
+    assert_snapshot!(
+        actual,
+        @r"
+    ProjectionExec: expr=[a@0 as x, b@1 as y]
+      FilterExec: b@1 > 10, projection=[a@0, b@1, c@2, d@3]
+        DataSourceExec: file_groups={1 group: [[x]]}, projection=[a, b, c, d, e], file_type=csv, has_header=false
+    "
+    );
+
+    let after_optimize =
+        ProjectionPushdown::new().optimize(projection, &ConfigOptions::new())?;
+    let after_optimize_string = displayable(after_optimize.as_ref())
+        .indent(true)
+        .to_string();
+    let actual = after_optimize_string.trim();
+    assert_snapshot!(
+        actual,
+        @r"
+    FilterExec: y@1 > 10
+      DataSourceExec: file_groups={1 group: [[x]]}, projection=[a@0 as x, b@1 as y], file_type=csv, has_header=false
+    "
+    );
+
+    Ok(())
+}
diff --git a/datafusion/expr/src/logical_plan/plan.rs b/datafusion/expr/src/logical_plan/plan.rs
index 07e0eb1a77aa9..4f73169ad2827 100644
--- a/datafusion/expr/src/logical_plan/plan.rs
+++ b/datafusion/expr/src/logical_plan/plan.rs
@@ -45,7 +45,7 @@ use crate::utils::{
     grouping_set_expr_count, grouping_set_to_exprlist, split_conjunction,
 };
 use crate::{
-    BinaryExpr, CreateMemoryTable, CreateView, Execute, Expr, ExprSchemable,
+    BinaryExpr, CreateMemoryTable, CreateView, Execute, Expr, ExprSchemable, GroupingSet,
     LogicalPlanBuilder, Operator, Prepare, TableProviderFilterPushDown, TableSource,
     WindowFunctionDefinition, build_join_schema, expr_vec_fmt, requalify_sides_if_needed,
 };
@@ -3595,11 +3595,12 @@ impl Aggregate {
                 .into_iter()
                 .map(|(q, f)| (q, f.as_ref().clone().with_nullable(true).into()))
                 .collect::<Vec<_>>();
+            let max_ordinal = max_grouping_set_duplicate_ordinal(&group_expr);
             qualified_fields.push((
                 None,
                 Field::new(
                     Self::INTERNAL_GROUPING_ID,
-                    Self::grouping_id_type(qualified_fields.len()),
+                    Self::grouping_id_type(qualified_fields.len(), max_ordinal),
                     false,
                 )
                 .into(),
@@ -3685,15 +3686,24 @@ impl Aggregate {
     }
 
     /// Returns the data type of the grouping id.
-    /// The grouping ID value is a bitmask where each set bit
-    /// indicates that the corresponding grouping expression is
-    /// null
-    pub fn grouping_id_type(group_exprs: usize) -> DataType {
-        if group_exprs <= 8 {
+    ///
+    /// The grouping ID packs two pieces of information into a single integer:
+    /// - The low `group_exprs` bits are the semantic bitmask (a set bit means the
+    ///   corresponding grouping expression is NULL for this grouping set).
+    /// - The bits above position `group_exprs` encode a duplicate ordinal that
+    ///   distinguishes multiple occurrences of the same grouping set pattern.
+    ///
+    /// `max_ordinal` is the highest ordinal value that will appear (0 when there
+    /// are no duplicate grouping sets).  The type is chosen to be the smallest
+    /// unsigned integer that can represent both parts.
+    pub fn grouping_id_type(group_exprs: usize, max_ordinal: usize) -> DataType {
+        let ordinal_bits = usize::BITS as usize - max_ordinal.leading_zeros() as usize;
+        let total_bits = group_exprs + ordinal_bits;
+        if total_bits <= 8 {
             DataType::UInt8
-        } else if group_exprs <= 16 {
+        } else if total_bits <= 16 {
             DataType::UInt16
-        } else if group_exprs <= 32 {
+        } else if total_bits <= 32 {
             DataType::UInt32
         } else {
             DataType::UInt64
@@ -3702,21 +3712,36 @@ impl Aggregate {
 
     /// Internal column used when the aggregation is a grouping set.
     ///
-    /// This column contains a bitmask where each bit represents a grouping
-    /// expression. The least significant bit corresponds to the rightmost
-    /// grouping expression. A bit value of 0 indicates that the corresponding
-    /// column is included in the grouping set, while a value of 1 means it is excluded.
+    /// This column packs two values into a single unsigned integer:
+    ///
+    /// - **Low bits (positions 0 .. n-1)**: a semantic bitmask where each bit
+    ///   represents one of the `n` grouping expressions.  The least significant
+    ///   bit corresponds to the rightmost grouping expression.  A `1` bit means
+    ///   the corresponding column is replaced with `NULL` for this grouping set;
+    ///   a `0` bit means it is included.
+    /// - **High bits (positions n and above)**: a *duplicate ordinal* that
+    ///   distinguishes multiple occurrences of the same semantic grouping set
+    ///   pattern within a single query.  The ordinal is `0` for the first
+    ///   occurrence, `1` for the second, and so on.
+    ///
+    /// The integer type is chosen by [`Self::grouping_id_type`] to be the
+    /// smallest `UInt8 / UInt16 / UInt32 / UInt64` that can represent both
+    /// parts.
     ///
-    /// For example, for the grouping expressions CUBE(a, b), the grouping ID
-    /// column will have the following values:
+    /// For example, for the grouping expressions CUBE(a, b) (no duplicates),
+    /// the grouping ID column will have the following values:
     ///     0b00: Both `a` and `b` are included
     ///     0b01: `b` is excluded
     ///     0b10: `a` is excluded
     ///     0b11: Both `a` and `b` are excluded
     ///
-    /// This internal column is necessary because excluded columns are replaced
-    /// with `NULL` values. To handle these cases correctly, we must distinguish
-    /// between an actual `NULL` value in a column and a column being excluded from the set.
+    /// When the same set appears twice and `n = 2`, the duplicate ordinal is
+    /// packed into bit 2:
+    ///     first occurrence:  `0b0_01` (ordinal = 0, mask = 0b01)
+    ///     second occurrence: `0b1_01` (ordinal = 1, mask = 0b01)
+    ///
+    /// The GROUPING function always masks the value with `(1 << n) - 1` before
+    /// interpreting it so the ordinal bits are invisible to user-facing SQL.
     pub const INTERNAL_GROUPING_ID: &'static str = "__grouping_id";
 }
 
@@ -3737,6 +3762,24 @@ impl PartialOrd for Aggregate {
     }
 }
 
+/// Returns the highest duplicate ordinal across all grouping sets in `group_expr`.
+///
+/// The ordinal for each occurrence of a grouping set pattern is its 0-based
+/// index among identical entries. For example, if the same set appears three
+/// times, the ordinals are 0, 1, 2 and this function returns 2.
+/// Returns 0 when no grouping set is duplicated.
+fn max_grouping_set_duplicate_ordinal(group_expr: &[Expr]) -> usize {
+    if let Some(Expr::GroupingSet(GroupingSet::GroupingSets(sets))) = group_expr.first() {
+        let mut counts: HashMap<&[Expr], usize> = HashMap::new();
+        for set in sets {
+            *counts.entry(set).or_insert(0) += 1;
+        }
+        counts.into_values().max().unwrap_or(0).saturating_sub(1)
+    } else {
+        0
+    }
+}
+
 /// Checks whether any expression in `group_expr` contains `Expr::GroupingSet`.
 fn contains_grouping_set(group_expr: &[Expr]) -> bool {
     group_expr
@@ -5053,6 +5096,14 @@ mod tests {
         );
     }
 
+    #[test]
+    fn grouping_id_type_accounts_for_duplicate_ordinal_bits() {
+        // 8 grouping columns fit in UInt8 when there are no duplicate ordinals,
+        // but adding one duplicate ordinal bit widens the type to UInt16.
+        assert_eq!(Aggregate::grouping_id_type(8, 0), DataType::UInt8);
+        assert_eq!(Aggregate::grouping_id_type(8, 1), DataType::UInt16);
+    }
+
     #[test]
     fn test_filter_is_scalar() {
         // test empty placeholder
diff --git a/datafusion/optimizer/src/analyzer/resolve_grouping_function.rs b/datafusion/optimizer/src/analyzer/resolve_grouping_function.rs
index 6b8ae3e8531bc..c12d7fd2ec2f6 100644
--- a/datafusion/optimizer/src/analyzer/resolve_grouping_function.rs
+++ b/datafusion/optimizer/src/analyzer/resolve_grouping_function.rs
@@ -99,10 +99,17 @@ fn replace_grouping_exprs(
     {
         match expr {
             Expr::AggregateFunction(ref function) if is_grouping_function(&expr) => {
+                let grouping_id_type = is_grouping_set
+                    .then(|| {
+                        schema
+                            .field_with_name(None, Aggregate::INTERNAL_GROUPING_ID)
+                            .map(|f| f.data_type().clone())
+                    })
+                    .transpose()?;
                 let grouping_expr = grouping_function_on_id(
                     function,
                     &group_expr_to_bitmap_index,
-                    is_grouping_set,
+                    grouping_id_type,
                 )?;
                 projection_exprs.push(Expr::Alias(Alias::new(
                     grouping_expr,
@@ -184,40 +191,44 @@ fn validate_args(
 fn grouping_function_on_id(
     function: &AggregateFunction,
     group_by_expr: &HashMap<&Expr, usize>,
-    is_grouping_set: bool,
+    // None means not a grouping set (result is always 0).
+    grouping_id_type: Option<DataType>,
 ) -> Result<Expr> {
     validate_args(function, group_by_expr)?;
     let args = &function.params.args;
 
     // Postgres allows grouping function for group by without grouping sets, the result is then
     // always 0
-    if !is_grouping_set {
+    let Some(grouping_id_type) = grouping_id_type else {
         return Ok(Expr::Literal(ScalarValue::from(0i32), None));
-    }
-
-    let group_by_expr_count = group_by_expr.len();
-    let literal = |value: usize| {
-        if group_by_expr_count < 8 {
-            Expr::Literal(ScalarValue::from(value as u8), None)
-        } else if group_by_expr_count < 16 {
-            Expr::Literal(ScalarValue::from(value as u16), None)
-        } else if group_by_expr_count < 32 {
-            Expr::Literal(ScalarValue::from(value as u32), None)
-        } else {
-            Expr::Literal(ScalarValue::from(value as u64), None)
-        }
     };
 
+    // Use the actual __grouping_id column type to size literals correctly. This
+    // accounts for duplicate-ordinal bits that `Aggregate::grouping_id_type`
+    // packs into the high bits of the column, which a simple count of grouping
+    // expressions would miss.
+    let literal = |value: usize| match &grouping_id_type {
+        DataType::UInt8 => Expr::Literal(ScalarValue::from(value as u8), None),
+        DataType::UInt16 => Expr::Literal(ScalarValue::from(value as u16), None),
+        DataType::UInt32 => Expr::Literal(ScalarValue::from(value as u32), None),
+        DataType::UInt64 => Expr::Literal(ScalarValue::from(value as u64), None),
+        other => panic!("unexpected __grouping_id type: {other}"),
+    };
     let grouping_id_column = Expr::Column(Column::from(Aggregate::INTERNAL_GROUPING_ID));
-    // The grouping call is exactly our internal grouping id
-    if args.len() == group_by_expr_count
+    if args.len() == group_by_expr.len()
         && args
             .iter()
             .rev()
             .enumerate()
             .all(|(idx, expr)| group_by_expr.get(expr) == Some(&idx))
     {
-        return Ok(cast(grouping_id_column, DataType::Int32));
+        let n = group_by_expr.len();
+        // Mask the ordinal bits above position `n` so only the semantic bitmask is visible.
+        // checked_shl returns None when n >= 64 (all bits are semantic), mapping to u64::MAX.
+        let semantic_mask: u64 = 1u64.checked_shl(n as u32).map_or(u64::MAX, |m| m - 1);
+        let masked_id =
+            bitwise_and(grouping_id_column.clone(), literal(semantic_mask as usize));
+        return Ok(cast(masked_id, DataType::Int32));
     }
 
     args.iter()
diff --git a/datafusion/optimizer/src/propagate_empty_relation.rs b/datafusion/optimizer/src/propagate_empty_relation.rs
index da18d9071869c..6565d4f187339 100644
--- a/datafusion/optimizer/src/propagate_empty_relation.rs
+++ b/datafusion/optimizer/src/propagate_empty_relation.rs
@@ -21,9 +21,9 @@ use std::sync::Arc;
 
 use datafusion_common::JoinType;
 use datafusion_common::tree_node::Transformed;
-use datafusion_common::{Result, plan_err};
+use datafusion_common::{Column, DFSchemaRef, Result, ScalarValue, plan_err};
 use datafusion_expr::logical_plan::LogicalPlan;
-use datafusion_expr::{EmptyRelation, Projection, Union};
+use datafusion_expr::{EmptyRelation, Expr, Projection, Union, cast, lit};
 
 use crate::optimizer::ApplyOrder;
 use crate::{OptimizerConfig, OptimizerRule};
@@ -73,12 +73,8 @@ impl OptimizerRule for PropagateEmptyRelation {
                 Ok(Transformed::no(plan))
             }
             LogicalPlan::Join(ref join) => {
-                // TODO: For Join, more join type need to be careful:
-                // For LeftOut/Full Join, if the right side is empty, the Join can be eliminated with a Projection with left side
-                // columns + right side columns replaced with null values.
-                // For RightOut/Full Join, if the left side is empty, the Join can be eliminated with a Projection with right side
-                // columns + left side columns replaced with null values.
                 let (left_empty, right_empty) = binary_plan_children_is_empty(&plan)?;
+                let left_field_count = join.left.schema().fields().len();
 
                 match join.join_type {
                     // For Full Join, only both sides are empty, the Join result is empty.
@@ -88,6 +84,24 @@ impl OptimizerRule for PropagateEmptyRelation {
                             schema: Arc::clone(&join.schema),
                         }),
                     )),
+                    // For Full Join, if one side is empty, replace with a
+                    // Projection that null-pads the empty side's columns.
+                    JoinType::Full if right_empty => {
+                        Ok(Transformed::yes(build_null_padded_projection(
+                            Arc::clone(&join.left),
+                            &join.schema,
+                            left_field_count,
+                            true,
+                        )?))
+                    }
+                    JoinType::Full if left_empty => {
+                        Ok(Transformed::yes(build_null_padded_projection(
+                            Arc::clone(&join.right),
+                            &join.schema,
+                            left_field_count,
+                            false,
+                        )?))
+                    }
                     JoinType::Inner if left_empty || right_empty => Ok(Transformed::yes(
                         LogicalPlan::EmptyRelation(EmptyRelation {
                             produce_one_row: false,
@@ -100,12 +114,32 @@ impl OptimizerRule for PropagateEmptyRelation {
                             schema: Arc::clone(&join.schema),
                         }),
                     )),
+                    // Left Join with empty right: all left rows survive
+                    // with NULLs for right columns.
+                    JoinType::Left if right_empty => {
+                        Ok(Transformed::yes(build_null_padded_projection(
+                            Arc::clone(&join.left),
+                            &join.schema,
+                            left_field_count,
+                            true,
+                        )?))
+                    }
                     JoinType::Right if right_empty => Ok(Transformed::yes(
                         LogicalPlan::EmptyRelation(EmptyRelation {
                             produce_one_row: false,
                             schema: Arc::clone(&join.schema),
                         }),
                     )),
+                    // Right Join with empty left: all right rows survive
+                    // with NULLs for left columns.
+                    JoinType::Right if left_empty => {
+                        Ok(Transformed::yes(build_null_padded_projection(
+                            Arc::clone(&join.right),
+                            &join.schema,
+                            left_field_count,
+                            false,
+                        )?))
+                    }
                     JoinType::LeftSemi if left_empty || right_empty => Ok(
                         Transformed::yes(LogicalPlan::EmptyRelation(EmptyRelation {
                             produce_one_row: false,
@@ -230,6 +264,57 @@ fn empty_child(plan: &LogicalPlan) -> Result<Option<LogicalPlan>> {
     }
 }
 
+/// Builds a Projection that replaces one side of an outer join with NULL literals.
+///
+/// When one side of an outer join is an `EmptyRelation`, the join can be eliminated
+/// by projecting the surviving side's columns as-is and replacing the empty side's
+/// columns with `CAST(NULL AS <type>)`.
+///
+/// The join schema is used as the projection's output schema to preserve nullability
+/// guarantees (important for FULL JOIN where the surviving side's columns are marked
+/// nullable in the join schema even if they aren't in the source schema).
+///
+/// # Example
+///
+/// For a `LEFT JOIN` where the right side is empty:
+/// ```text
+/// Left Join (orders.id = returns.order_id)        Projection(orders.id, orders.amount,
+///   ├── TableScan: orders                   =>      CAST(NULL AS Int64) AS order_id,
+///   └── EmptyRelation                               CAST(NULL AS Utf8) AS reason)
+///                                                   └── TableScan: orders
+/// ```
+fn build_null_padded_projection(
+    surviving_plan: Arc<LogicalPlan>,
+    join_schema: &DFSchemaRef,
+    left_field_count: usize,
+    empty_side_is_right: bool,
+) -> Result<LogicalPlan> {
+    let exprs = join_schema
+        .iter()
+        .enumerate()
+        .map(|(i, (qualifier, field))| {
+            let on_empty_side = if empty_side_is_right {
+                i >= left_field_count
+            } else {
+                i < left_field_count
+            };
+
+            if on_empty_side {
+                cast(lit(ScalarValue::Null), field.data_type().clone())
+                    .alias_qualified(qualifier.cloned(), field.name())
+            } else {
+                Expr::Column(Column::new(qualifier.cloned(), field.name()))
+            }
+        })
+        .collect::<Vec<_>>();
+
+    Ok(LogicalPlan::Projection(Projection::try_new_with_schema(
+        exprs,
+        surviving_plan,
+        Arc::clone(join_schema),
+    )?))
+}
+
 #[cfg(test)]
 mod tests {
 
@@ -570,6 +655,111 @@ mod tests {
         assert_empty_left_empty_right_lp(true, false, JoinType::RightAnti, false)
     }
 
+    #[test]
+    fn test_left_join_right_empty_null_pad() -> Result<()> {
+        let left =
+            LogicalPlanBuilder::from(test_table_scan_with_name("left")?).build()?;
+        let right_empty = LogicalPlanBuilder::from(test_table_scan_with_name("right")?)
+            .filter(lit(false))?
+            .build()?;
+
+        let plan = LogicalPlanBuilder::from(left)
+            .join_using(
+                right_empty,
+                JoinType::Left,
+                vec![Column::from_name("a".to_string())],
+            )?
+            .build()?;
+
+        let expected = "Projection: left.a, left.b, left.c, CAST(NULL AS UInt32) AS a, CAST(NULL AS UInt32) AS b, CAST(NULL AS UInt32) AS c\n  TableScan: left";
+        assert_together_optimized_plan(plan, expected, true)
+    }
+
+    #[test]
+    fn test_right_join_left_empty_null_pad() -> Result<()> {
+        let left_empty = LogicalPlanBuilder::from(test_table_scan_with_name("left")?)
+            .filter(lit(false))?
+            .build()?;
+        let right =
+            LogicalPlanBuilder::from(test_table_scan_with_name("right")?).build()?;
+
+        let plan = LogicalPlanBuilder::from(left_empty)
+            .join_using(
+                right,
+                JoinType::Right,
+                vec![Column::from_name("a".to_string())],
+            )?
+            .build()?;
+
+        let expected = "Projection: CAST(NULL AS UInt32) AS a, CAST(NULL AS UInt32) AS b, CAST(NULL AS UInt32) AS c, right.a, right.b, right.c\n  TableScan: right";
+        assert_together_optimized_plan(plan, expected, true)
+    }
+
+    #[test]
+    fn test_full_join_right_empty_null_pad() -> Result<()> {
+        let left =
+            LogicalPlanBuilder::from(test_table_scan_with_name("left")?).build()?;
+        let right_empty = LogicalPlanBuilder::from(test_table_scan_with_name("right")?)
+            .filter(lit(false))?
+            .build()?;
+
+        let plan = LogicalPlanBuilder::from(left)
+            .join_using(
+                right_empty,
+                JoinType::Full,
+                vec![Column::from_name("a".to_string())],
+            )?
+            .build()?;
+
+        let expected = "Projection: left.a, left.b, left.c, CAST(NULL AS UInt32) AS a, CAST(NULL AS UInt32) AS b, CAST(NULL AS UInt32) AS c\n  TableScan: left";
+        assert_together_optimized_plan(plan, expected, true)
+    }
+
+    #[test]
+    fn test_full_join_left_empty_null_pad() -> Result<()> {
+        let left_empty = LogicalPlanBuilder::from(test_table_scan_with_name("left")?)
+            .filter(lit(false))?
+            .build()?;
+        let right =
+            LogicalPlanBuilder::from(test_table_scan_with_name("right")?).build()?;
+
+        let plan = LogicalPlanBuilder::from(left_empty)
+            .join_using(
+                right,
+                JoinType::Full,
+                vec![Column::from_name("a".to_string())],
+            )?
+            .build()?;
+
+        let expected = "Projection: CAST(NULL AS UInt32) AS a, CAST(NULL AS UInt32) AS b, CAST(NULL AS UInt32) AS c, right.a, right.b, right.c\n  TableScan: right";
+        assert_together_optimized_plan(plan, expected, true)
+    }
+
+    #[test]
+    fn test_left_join_complex_on_right_empty_null_pad() -> Result<()> {
+        let left =
+            LogicalPlanBuilder::from(test_table_scan_with_name("left")?).build()?;
+        let right_empty = LogicalPlanBuilder::from(test_table_scan_with_name("right")?)
+            .filter(lit(false))?
+            .build()?;
+
+        // Complex ON condition: left.a = right.a AND left.b > right.b
+        let plan = LogicalPlanBuilder::from(left)
+            .join(
+                right_empty,
+                JoinType::Left,
+                (
+                    vec![Column::from_name("a".to_string())],
+                    vec![Column::from_name("a".to_string())],
+                ),
+                Some(col("left.b").gt(col("right.b"))),
+            )?
+            .build()?;
+
+        let expected = "Projection: left.a, left.b, left.c, CAST(NULL AS UInt32) AS a, CAST(NULL AS UInt32) AS b, CAST(NULL AS UInt32) AS c\n  TableScan: left";
+        assert_together_optimized_plan(plan, expected, true)
+    }
+
     #[test]
     fn test_empty_with_non_empty() -> Result<()> {
         let table_scan = test_table_scan()?;
diff --git a/datafusion/physical-plan/src/aggregates/mod.rs b/datafusion/physical-plan/src/aggregates/mod.rs
index a3f0f568616e5..41782330c39da 100644
--- a/datafusion/physical-plan/src/aggregates/mod.rs
+++ b/datafusion/physical-plan/src/aggregates/mod.rs
@@ -37,7 +37,7 @@ use crate::{
 use datafusion_common::config::ConfigOptions;
 use datafusion_physical_expr::utils::collect_columns;
 use parking_lot::Mutex;
-use std::collections::HashSet;
+use std::collections::{HashMap, HashSet};
 
 use arrow::array::{ArrayRef, UInt8Array, UInt16Array, UInt32Array, UInt64Array};
 use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
@@ -396,6 +396,15 @@ impl PhysicalGroupBy {
         self.expr.len() + usize::from(self.has_grouping_set)
     }
 
+    /// Returns the Arrow data type of the `__grouping_id` column.
+    ///
+    /// The type is chosen to be wide enough to hold both the semantic bitmask
+    /// (in the low `n` bits, where `n` is the number of grouping expressions)
+    /// and the duplicate ordinal (in the high bits).
+    fn grouping_id_data_type(&self) -> DataType {
+        Aggregate::grouping_id_type(self.expr.len(), max_duplicate_ordinal(&self.groups))
+    }
+
     pub fn group_schema(&self, schema: &Schema) -> Result<SchemaRef> {
         Ok(Arc::new(Schema::new(self.group_fields(schema)?)))
     }
@@ -420,7 +429,7 @@ impl PhysicalGroupBy {
             fields.push(
                 Field::new(
                     Aggregate::INTERNAL_GROUPING_ID,
-                    Aggregate::grouping_id_type(self.expr.len()),
+                    self.grouping_id_data_type(),
                     false,
                 )
                 .into(),
@@ -2039,27 +2048,72 @@ fn evaluate_optional(
         .collect()
 }
 
-fn group_id_array(group: &[bool], batch: &RecordBatch) -> Result<ArrayRef> {
-    if group.len() > 64 {
+/// Builds the internal `__grouping_id` array for a single grouping set.
+///
+/// The returned array packs two values into a single integer:
+///
+/// - Low `n` bits (positions 0 .. n-1): the semantic bitmask.  A `1` bit
+///   at position `i` means that the `i`-th grouping column (counting from the
+///   least significant bit, i.e. the *last* column in the `group` slice) is
+///   `NULL` for this grouping set.
+/// - High bits (positions n and above): the duplicate `ordinal`, which
+///   distinguishes multiple occurrences of the same grouping-set pattern.  The
+///   ordinal is `0` for the first occurrence, `1` for the second, and so on.
+///
+/// The integer type is chosen to be the smallest `UInt8 / UInt16 / UInt32 /
+/// UInt64` that can represent both parts.  It matches the type returned by
+/// [`Aggregate::grouping_id_type`].
+fn group_id_array(
+    group: &[bool],
+    ordinal: usize,
+    max_ordinal: usize,
+    batch: &RecordBatch,
+) -> Result<ArrayRef> {
+    let n = group.len();
+    if n > 64 {
         return not_impl_err!(
             "Grouping sets with more than 64 columns are not supported"
         );
     }
-    let group_id = group.iter().fold(0u64, |acc, &is_null| {
+    let ordinal_bits = usize::BITS as usize - max_ordinal.leading_zeros() as usize;
+    let total_bits = n + ordinal_bits;
+    if total_bits > 64 {
+        return not_impl_err!(
+            "Grouping sets with {n} columns and a maximum duplicate ordinal of \
+             {max_ordinal} require {total_bits} bits, which exceeds 64"
+        );
+    }
+    let semantic_id = group.iter().fold(0u64, |acc, &is_null| {
         (acc << 1) | if is_null { 1 } else { 0 }
     });
+    let full_id = semantic_id | ((ordinal as u64) << n);
     let num_rows = batch.num_rows();
-    if group.len() <= 8 {
-        Ok(Arc::new(UInt8Array::from(vec![group_id as u8; num_rows])))
-    } else if group.len() <= 16 {
-        Ok(Arc::new(UInt16Array::from(vec![group_id as u16; num_rows])))
-    } else if group.len() <= 32 {
-        Ok(Arc::new(UInt32Array::from(vec![group_id as u32; num_rows])))
+    if total_bits <= 8 {
+        Ok(Arc::new(UInt8Array::from(vec![full_id as u8; num_rows])))
+    } else if total_bits <= 16 {
+        Ok(Arc::new(UInt16Array::from(vec![full_id as u16; num_rows])))
+    } else if total_bits <= 32 {
+        Ok(Arc::new(UInt32Array::from(vec![full_id as u32; num_rows])))
     } else {
-        Ok(Arc::new(UInt64Array::from(vec![group_id; num_rows])))
+        Ok(Arc::new(UInt64Array::from(vec![full_id; num_rows])))
     }
 }
 
+/// Returns the highest duplicate ordinal across all grouping sets.
+///
+/// At the call-site, the ordinal is the 0-based index assigned to each
+/// occurrence of a repeated grouping-set pattern: the first occurrence gets
+/// ordinal 0, the second gets 1, and so on.  If the same `Vec<bool>` appears
+/// three times the ordinals are 0, 1, 2 and this function returns 2.
+/// Returns 0 when no grouping set is duplicated.
+fn max_duplicate_ordinal(groups: &[Vec<bool>]) -> usize {
+    let mut counts: HashMap<&[bool], usize> = HashMap::new();
+    for group in groups {
+        *counts.entry(group).or_insert(0) += 1;
+    }
+    counts.into_values().max().unwrap_or(0).saturating_sub(1)
+}
+
 /// Evaluate a group by expression against a `RecordBatch`
 ///
 /// Arguments:
@@ -2074,6 +2128,8 @@ pub fn evaluate_group_by(
     group_by: &PhysicalGroupBy,
     batch: &RecordBatch,
 ) -> Result<Vec<Vec<ArrayRef>>> {
+    let max_ordinal = max_duplicate_ordinal(&group_by.groups);
+    let mut ordinal_per_pattern: HashMap<&[bool], usize> = HashMap::new();
     let exprs = evaluate_expressions_to_arrays(
         group_by.expr.iter().map(|(expr, _)| expr),
         batch,
@@ -2087,6 +2143,10 @@ pub fn evaluate_group_by(
         .groups
         .iter()
         .map(|group| {
+            let ordinal = ordinal_per_pattern.entry(group).or_insert(0);
+            let current_ordinal = *ordinal;
+            *ordinal += 1;
+
             let mut group_values = Vec::with_capacity(group_by.num_group_exprs());
             group_values.extend(group.iter().enumerate().map(|(idx, is_null)| {
                 if *is_null {
@@ -2096,7 +2156,12 @@ pub fn evaluate_group_by(
                 }
             }));
             if !group_by.is_single() {
-                group_values.push(group_id_array(group, batch)?);
+                group_values.push(group_id_array(
+                    group,
+                    current_ordinal,
+                    max_ordinal,
+                    batch,
+                )?);
             }
             Ok(group_values)
         })
diff --git a/datafusion/physical-plan/src/filter.rs b/datafusion/physical-plan/src/filter.rs
index afe2b0ae810a3..8720d5f7d223b 100644
--- a/datafusion/physical-plan/src/filter.rs
+++ b/datafusion/physical-plan/src/filter.rs
@@ -568,6 +568,10 @@ impl ExecutionPlan for FilterExec {
                 return FilterExecBuilder::from(self)
                     .with_input(make_with_child(projection, self.input())?)
                     .with_predicate(new_predicate)
+                    // The original FilterExec projection referenced columns from its old
+                    // input. After the swap the new input is the ProjectionExec which
+                    // already handles column selection, so clear the projection here.
+                    .apply_projection(None)?
                     .build()
                     .map(|e| Some(Arc::new(e) as _));
             }
@@ -2572,4 +2576,67 @@ mod tests {
         );
         Ok(())
     }
+
+    /// Regression test: ProjectionExec on top of a FilterExec that already has
+    /// an explicit projection must not panic when `try_swapping_with_projection`
+    /// attempts to swap the two nodes.
+    ///
+    /// Before the fix, `FilterExecBuilder::from(self)` copied the old projection
+    /// (e.g. `[0, 1, 2]`) from the FilterExec. After `.with_input` replaced the
+    /// input with the narrower ProjectionExec (2 columns), `.build()` tried to
+    /// validate the stale `[0, 1, 2]` projection against the 2-column schema and
+    /// panicked with "project index 2 out of bounds, max field 2".
+    #[test]
+    fn test_filter_with_projection_swap_does_not_panic() -> Result<()> {
+        use crate::projection::ProjectionExpr;
+        use datafusion_physical_expr::expressions::col;
+
+        // Schema: [ts: Int64, tokens: Int64, svc: Utf8]
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("ts", DataType::Int64, false),
+            Field::new("tokens", DataType::Int64, false),
+            Field::new("svc", DataType::Utf8, false),
+        ]));
+        let input = Arc::new(EmptyExec::new(Arc::clone(&schema)));
+
+        // FilterExec: ts > 0, projection=[ts@0, tokens@1, svc@2] (all 3 cols)
+        let predicate = Arc::new(BinaryExpr::new(
+            Arc::new(Column::new("ts", 0)),
+            Operator::Gt,
+            Arc::new(Literal::new(ScalarValue::Int64(Some(0)))),
+        ));
+        let filter = Arc::new(
+            FilterExecBuilder::new(predicate, input)
+                .apply_projection(Some(vec![0, 1, 2]))?
+                .build()?,
+        );
+
+        // ProjectionExec: narrows to [ts, tokens] (drops svc)
+        let proj_exprs = vec![
+            ProjectionExpr {
+                expr: col("ts", &filter.schema())?,
+                alias: "ts".to_string(),
+            },
+            ProjectionExpr {
+                expr: col("tokens", &filter.schema())?,
+                alias: "tokens".to_string(),
+            },
+        ];
+        let projection = Arc::new(ProjectionExec::try_new(
+            proj_exprs,
+            Arc::clone(&filter) as _,
+        )?);
+
+        // This must not panic
+        let result = filter.try_swapping_with_projection(&projection)?;
+        assert!(result.is_some(), "swap should succeed");
+
+        let new_plan = result.unwrap();
+        // Output schema must still be [ts, tokens]
+        let out_schema = new_plan.schema();
+        assert_eq!(out_schema.fields().len(), 2);
+        assert_eq!(out_schema.field(0).name(), "ts");
+        assert_eq!(out_schema.field(1).name(), "tokens");
+        Ok(())
+    }
 }
diff --git a/datafusion/sqllogictest/test_files/group_by.slt b/datafusion/sqllogictest/test_files/group_by.slt
index 59db63ba420e9..b313424951532 100644
--- a/datafusion/sqllogictest/test_files/group_by.slt
+++ b/datafusion/sqllogictest/test_files/group_by.slt
@@ -5203,6 +5203,41 @@ NULL NULL 1
 statement ok
 drop table t;
 
+# regression: duplicate grouping sets must not be collapsed into one
+statement ok
+create table duplicate_grouping_sets(deptno int, job varchar, sal int, comm int) as values
+(10, 'CLERK', 1300, null),
+(20, 'MANAGER', 3000, null);
+
+query ITIIIII
+select deptno, job, sal, sum(comm), grouping(deptno), grouping(job), grouping(sal)
+from duplicate_grouping_sets
+group by grouping sets ((deptno, job), (deptno, sal), (deptno, job))
+order by deptno, job, sal, grouping(deptno), grouping(job), grouping(sal);
+----
+10 CLERK NULL NULL 0 0 1
+10 CLERK NULL NULL 0 0 1
+10 NULL 1300 NULL 0 1 0
+20 MANAGER NULL NULL 0 0 1
+20 MANAGER NULL NULL 0 0 1
+20 NULL 3000 NULL 0 1 0
+
+query ITII
+select deptno, job, sal, grouping(deptno, job, sal)
+from duplicate_grouping_sets
+group by grouping sets ((deptno, job), (deptno, sal), (deptno, job))
+order by deptno, job, sal, grouping(deptno, job, sal);
+----
+10 CLERK NULL 1
+10 CLERK NULL 1
+10 NULL 1300 2
+20 MANAGER NULL 1
+20 MANAGER NULL 1
+20 NULL 3000 2
+
+statement ok
+drop table duplicate_grouping_sets;
+
 # test multi group by for binary type without nulls
 statement ok
 create table t(a int, b bytea) as values (1, 0xa), (1, 0xa), (2, 0xb), (3, 0xb), (3, 0xb);
diff --git a/datafusion/sqllogictest/test_files/propagate_empty_relation_outer_join.slt b/datafusion/sqllogictest/test_files/propagate_empty_relation_outer_join.slt
new file mode 100644
index 0000000000000..016a3108e509b
--- /dev/null
+++ b/datafusion/sqllogictest/test_files/propagate_empty_relation_outer_join.slt
@@ -0,0 +1,155 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+
+#   http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# Test PropagateEmptyRelation rule: outer joins where one side is
+# an EmptyRelation should be replaced with a null-padded projection.
+
+statement ok
+create table t1(a int, b varchar, c double);
+
+statement ok
+create table t2(x int, y varchar, z double);
+
+statement ok
+insert into t1 values (1, 'a', 10.0), (2, 'b', 20.0), (3, 'c', 30.0);
+
+statement ok
+insert into t2 values (1, 'p', 100.0), (2, 'q', 200.0);
+
+statement ok
+set datafusion.explain.logical_plan_only = true;
+
+###
+### LEFT JOIN with empty right (WHERE false subquery)
+###
+
+# The join should be eliminated — no join operator in the plan
+query TT
+explain select * from t1 left join (select * from t2 where false) r on t1.a = r.x;
+----
+logical_plan
+01)Projection: t1.a, t1.b, t1.c, Int32(NULL) AS x, Utf8View(NULL) AS y, Float64(NULL) AS z
+02)--TableScan: t1 projection=[a, b, c]
+
+# Verify result correctness — all left rows with NULLs on right
+query ITRITR rowsort
+select * from t1 left join (select * from t2 where false) r on t1.a = r.x;
+----
+1 a 10 NULL NULL NULL
+2 b 20 NULL NULL NULL
+3 c 30 NULL NULL NULL
+
+###
+### RIGHT JOIN with empty left
+###
+
+query TT
+explain select * from (select * from t1 where false) l right join t2 on l.a = t2.x;
+----
+logical_plan
+01)Projection: Int32(NULL) AS a, Utf8View(NULL) AS b, Float64(NULL) AS c, t2.x, t2.y, t2.z
+02)--TableScan: t2 projection=[x, y, z]
+
+query ITRITR rowsort
+select * from (select * from t1 where false) l right join t2 on l.a = t2.x;
+----
+NULL NULL NULL 1 p 100
+NULL NULL NULL 2 q 200
+
+###
+### FULL JOIN with empty right
+###
+
+query TT
+explain select * from t1 full join (select * from t2 where false) r on t1.a = r.x;
+----
+logical_plan
+01)Projection: t1.a, t1.b, t1.c, Int32(NULL) AS x, Utf8View(NULL) AS y, Float64(NULL) AS z
+02)--TableScan: t1 projection=[a, b, c]
+
+query ITRITR rowsort
+select * from t1 full join (select * from t2 where false) r on t1.a = r.x;
+----
+1 a 10 NULL NULL NULL
+2 b 20 NULL NULL NULL
+3 c 30 NULL NULL NULL
+
+###
+### FULL JOIN with empty left
+###
+
+query TT
+explain select * from (select * from t1 where false) l full join t2 on l.a = t2.x;
+----
+logical_plan
+01)Projection: Int32(NULL) AS a, Utf8View(NULL) AS b, Float64(NULL) AS c, t2.x, t2.y, t2.z
+02)--TableScan: t2 projection=[x, y, z]
+
+query ITRITR rowsort
+select * from (select * from t1 where false) l full join t2 on l.a = t2.x;
+----
+NULL NULL NULL 1 p 100
+NULL NULL NULL 2 q 200
+
+###
+### Filter on top of optimized join
+###
+
+query TT
+explain select * from t1 left join (select * from t2 where false) r on t1.a = r.x where t1.a > 1;
+----
+logical_plan
+01)Projection: t1.a, t1.b, t1.c, Int32(NULL) AS x, Utf8View(NULL) AS y, Float64(NULL) AS z
+02)--Filter: t1.a > Int32(1)
+03)----TableScan: t1 projection=[a, b, c]
+
+query ITRITR rowsort
+select * from t1 left join (select * from t2 where false) r on t1.a = r.x where t1.a > 1;
+----
+2 b 20 NULL NULL NULL
+3 c 30 NULL NULL NULL
+
+###
+### LEFT JOIN with complex ON condition and empty right
+###
+
+query TT
+explain select * from t1 left join (select * from t2 where false) r on t1.a = r.x and t1.c > r.z;
+----
+logical_plan
+01)Projection: t1.a, t1.b, t1.c, Int32(NULL) AS x, Utf8View(NULL) AS y, Float64(NULL) AS z
+02)--TableScan: t1 projection=[a, b, c]
+
+query ITRITR rowsort
+select * from t1 left join (select * from t2 where false) r on t1.a = r.x and t1.c > r.z;
+----
+1 a 10 NULL NULL NULL
+2 b 20 NULL NULL NULL
+3 c 30 NULL NULL NULL
+
+###
+### Cleanup
+###
+
+statement ok
+set datafusion.explain.logical_plan_only = false;
+
+statement ok
+drop table t1;
+
+statement ok
+drop table t2;
diff --git a/datafusion/sqllogictest/test_files/subquery.slt b/datafusion/sqllogictest/test_files/subquery.slt
index 5d54de6dbce2d..368d252e25006 100644
--- a/datafusion/sqllogictest/test_files/subquery.slt
+++ b/datafusion/sqllogictest/test_files/subquery.slt
@@ -688,10 +688,8 @@ query TT
 explain SELECT t1_id, (SELECT t2_id FROM t2 limit 0) FROM t1
 ----
 logical_plan
-01)Projection: t1.t1_id, __scalar_sq_1.t2_id AS t2_id
-02)--Left Join: 
-03)----TableScan: t1 projection=[t1_id]
-04)----EmptyRelation: rows=0
+01)Projection: t1.t1_id, Int32(NULL) AS t2_id
+02)--TableScan: t1 projection=[t1_id]
 
 query II rowsort
 SELECT t1_id, (SELECT t2_id FROM t2 limit 0) FROM t1
diff --git a/uv.lock b/uv.lock
index 925d850bba42d..d8d2cc13cf72b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -240,61 +240,61 @@ wheels = [
 
 [[package]]
 name = "cryptography"
-version = "46.0.6"
+version = "46.0.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cffi", marker = "platform_python_implementation != 'PyPy'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/a4/ba/04b1bd4218cbc58dc90ce967106d51582371b898690f3ae0402876cc4f34/cryptography-46.0.6.tar.gz", hash = "sha256:27550628a518c5c6c903d84f637fbecf287f6cb9ced3804838a1295dc1fd0759", size = 750542, upload-time = "2026-03-25T23:34:53.396Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/47/93/ac8f3d5ff04d54bc814e961a43ae5b0b146154c89c61b47bb07557679b18/cryptography-46.0.7.tar.gz", hash = "sha256:e4cfd68c5f3e0bfdad0d38e023239b96a2fe84146481852dffbcca442c245aa5", size = 750652, upload-time = "2026-04-08T01:57:54.692Z" }
 wheels = [
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