Which issue does this PR close?

Closes #20184

Rationale for this change

ExecutionPlan::partition_statistics forces each operator to re-fetch child statistics internally, causing exponential recomputation in deep plans and making it impossible to inject enriched statistics from external sources (e.g., expression-level analyzers, custom statistics providers).

What changes are included in this PR?

Breaking change: the ExecutionPlan::partition_statistics signature changes from (&self, partition: Option<usize>) to (&self, partition: Option<usize>, ctx: &StatisticsContext). Migration guide added to docs/source/library-user-guide/upgrading/54.0.0.md.

Add a StatisticsContext parameter to partition_statistics that carries pre-computed child statistics, and a compute_statistics() utility that walks the plan tree bottom-up, threading child statistics through the context automatically. StatisticsContext carries one Arc<Statistics> per child node and is designed to be extended with additional context (e.g., expression-level analyzers, custom statistics providers) without further signature changes.

Operator categories

• Leaf nodes (EmptyExec, PlaceholderRowExec, WorkTableExec, DataSourceExec): ignore the context, return their own stats. DataSourceExec delegates to the DataSource trait which has a separate partition_statistics that was not changed.
• Passthrough operators (BufferExec, CooperativeExec, PartialSortExec, OutputRequirementExec): return ctx.child_stats()[0] directly.
• Transform operators (FilterExec, ProjectionExec, AggregateExec, WindowAggExec, CoalesceBatchesExec, GlobalLimitExec, LocalLimitExec): use ctx.child_stats()[0] as input, then apply their transformation (selectivity, column projection, grouping cardinality, fetch limit, etc.).
• Partition-merging operators (CoalescePartitionsExec, SortPreservingMergeExec, SortExec with !preserve_partitioning, RepartitionExec): always need overall child stats regardless of which output partition is requested, since they merge/redistribute input partitions. These call compute_statistics(child, None) internally instead of using the context.
• Symmetric joins (SortMergeJoinExec): both sides use the same partition, so ctx.child_stats() is correct for both None and Some(i) cases.
• Asymmetric joins (HashJoinExec CollectLeft, CrossJoinExec, NestedLoopJoinExec): the left (broadcast) side always needs overall stats, so they call compute_statistics(left, None) for the Some case. The right side is partitioned and uses ctx.child_stats()[1] directly. HashJoinExec Partitioned mode is symmetric (both use context). HashJoinExec Auto mode needs overall stats from both sides.
• Union/Interleave: UnionExec uses ctx.child_stats() for the None case (reduces with stats_union). For Some(partition), Union remaps partition indices across children and calls compute_statistics on the specific child with the remapped index. InterleaveExec uses ctx.child_stats() directly (symmetric across all inputs).

Callers

All direct plan.partition_statistics(None) calls in optimizer rules (JoinSelection, AggregateStatistics, EnforceDistribution), display code, StatisticsRegistry, and tests are replaced with compute_statistics(plan, None).

Tests

No new tests added. This is a no-op refactoring confirmed by all existing tests passing unchanged across all affected crates (datafusion-physical-plan, datafusion-physical-optimizer, datafusion, datafusion-datasource).

What remains for follow-up

• Adding expression-level analyzers and custom statistics providers to StatisticsContext (eliminates the separate StatisticsRegistry tree walk and the ExpressionAnalyzer injection machinery from Add ExpressionAnalyzer for pluggable expression-level statistics estimation #21122)
• Extending DataSource::partition_statistics with context if needed
• Caching to avoid redundant compute_statistics(child, None) calls: partition-merging operators (CoalescePartitions, SortPreservingMerge, etc.) and asymmetric joins (HashJoin CollectLeft, CrossJoin, NestedLoopJoin) currently call compute_statistics(child, None) internally when the requested partition is Some, triggering a separate bottom-up walk. A cache on StatisticsContext keyed by (plan node, partition) would let these reuse already-computed results.

Test plan

• cargo fmt --all
• cargo clippy --all-targets --all-features -- -D warnings (affected crates)
• cargo test --profile ci on datafusion-physical-plan, datafusion-physical-optimizer, datafusion, datafusion-datasource

Disclaimer: I used AI to assist in the code generation, I have manually reviewed the output and it matches my intention and understanding.