perf: nested struct statistics pruning

[peterxcli]

Which issue does this PR close?

Related to #5739 and #5553. This draft demonstrates nested-field statistics pruning, but does not close either issue: checked TIMESTAMP_MILLIS conversion and Spark filtering parity still need separate work.

Rationale for this change

Comet already serializes nested predicates, but its custom GetStructField expression does not expose DataFusion's field-access capability. A selective s.inner.k filter therefore reads all row groups even when leaf statistics can exclude most of them.

This draft connects Comet to peterxcli/datafusion#2 to demonstrate the performance and correctness of the integration. It pins that DataFusion fork and needs the upstream dependency work before it is ready to merge.

On a synthetic local Parquet file with 4,194,304 rows and 64 row groups, the selective nested query improves from 642.3 ms to 182.6 ms (3.52x). It prunes 63/64 row groups and reduces reader bytes from 182,481,422 to 2,851,301 (98.4% less), with identical aggregate checksums.

Query	Baseline median	Candidate median	Baseline / candidate
Nested filter, sorted keys	642.3 ms	182.6 ms	3.52x
Top-level filter, sorted keys	138.8 ms	138.3 ms	1.00x
Projection only	530.2 ms	537.4 ms	0.99x
Nested filter, shuffled keys	585.4 ms	577.1 ms	1.01x

Disabling pruning in the candidate restores all 64 groups and baseline reader bytes, with a 638.0 ms median over five control measurements. The other matched comparisons differ by less than 2%, with overlapping interquartile ranges.

These are warm-cache measurements on an AMD EPYC 7282 host with Spark 4.1.3, local[1], matching release build settings, and 20 samples per revision in alternating run order. Timings include SQL planning, execution, and collecting the result. Every measured plan uses CometNativeScan. This does not establish production or object-storage speedups, or improvements for arrays, maps, Variant, or unfiltered scans.

What changes are included in this PR?

• Pin DataFusion packages to fork revision 0b0506a9acab9d5892ecf7e89243c3b34664bcc6.
• Expose struct field access through a scalar UDF while preserving Comet's parent-null handling. Resolve fields by name after schema adaptation and retain ordinal access for duplicate names.
• Route native GetStructField planning through the adapter and add a real scan regression for nested statistics pruning.
• Include the benchmark harness, raw timings, plans, metrics, checksums, hashes, and reproduction instructions in benchmarks/nested-pruning.
• Correct comments about the remaining nested timestamp overflow limitation; conversion behavior is unchanged.

How are these changes tested?

• Four Rust expression tests passed, including parent nulls, scalar structs, reordered fields, and duplicate field names.
• Five Spark tests passed, covering nested pruning enabled/disabled, nested schema evolution, field-ID mapping, and nullable struct access; one pre-existing test was ignored.
• Native release builds and Maven packaging passed. Rust formatting and Spark style/format checks passed.
• The benchmark checks aggregate results and pruning counters, including candidate pruning-disabled and decoder-filter-enabled controls. The latter are correctness controls, not a matched decoder-filter performance comparison.

Build, test, and benchmark results were collected against Comet bb9e74020adc228e486f6f4d0fa68292b30bff31 and the patch on that base. The baseline uses its checked-in DataFusion 55.0.0 dependencies, so the comparison includes both the DataFusion fork and the Comet adapter. Before publication, the patch was rebased unchanged onto 5af898b2e (the intervening Iceberg unknown-transform fix); git range-diff confirms an identical patch. Formatting and diff checks were repeated after the rebase; builds and benchmark timings were not rerun.