refactor: consolidate and strengthen Iceberg statistics tests

[alexanderbianchi]

Less test code, stronger statistics coverage

Based directly on iceberg-0.10 at f9340ef, including merged #715 and #716. “Before” refers to that base. Only iceberg/tests/statistics.rs changes, with a net reduction of 24 lines.

Test / edge case	Before	After
Row count and full-scan byte size	Separate enabled/disabled test bodies	Ordinary named tests share assertions and retain both modes
Full-schema column statistics	Vector length only, in another pair of tests	Complete expected vector: populated metrics and unknown columns
Reordered projection / nonconsecutive selected field IDs	Projected vector length and row count	Exact association of metrics with reordered columns using IDs 4, 1, and 5
Null-count aggregation across files	None	Exact sum of 5
Column-size aggregation across files	None	Inexact sums of 400 and 600
Min/max bounds and scalar types	None	Int32 and Int64 bounds checked with their precision; opposing extrema order exercises both updating and retaining bounds
One file missing a column's null count	None	Aggregate stays Absent, rather than treating missing as zero
Entirely missing column metrics	Only an all-empty-metrics fixture	Unknown column alongside columns with known metrics
Column statistics disabled	Fixture had no metrics even when enabled	Populated fixture must return unknown column statistics when disabled
Explicit current-snapshot selection	Separate duplicate row-total test	Fixture selects the snapshot explicitly in every matrix case
Missing snapshot summary totals	Present	Retained
Filter and projection/sort propagation	Present	Retained
COUNT(*) skips the scan	Duplicated enabled/disabled bodies and snapshots	Both named cases retained, sharing assertions
Explain formatting	Two identical snapshots plus weak label checks	One diagnostic snapshot; computed values verified by the matrix

Structure

• Four ordinary named Tokio tests cover full scan / reordered projection × column statistics enabled / disabled. Two more named tests cover COUNT(*) scan elimination. No test_case macros or dependency.
• Session setup uses the merged harness's configure_session(...); tests never access its private context.
• Native Iceberg writers create a manifest with two synthetic data-file entries and a matching manifest list. refactor: build Iceberg test metadata at runtime #700's with_file supplies the bytes to the harness.
• Assertions observe query-output statistics, removing the recursive search/downcast helper and also checking projection propagation.
• insta::allow_duplicates! is needed only for the shared, identical COUNT(*) inline snapshots.
• The fixture is planning-only: synthetic Parquet paths are not opened. Multi-manifest merging, delete files, NDV, and verification against actual Parquet contents are not claimed here.
• The genuinely historical-snapshot regression remains in refactor: construct historical Iceberg fixtures in tests #702; this PR consolidates the current-snapshot-only check from feat[iceberg]: table statistics suite #687.

Validation

• cargo test -p datafusion-distributed-iceberg --test statistics --locked — all 10 cases passed
• cargo test -p datafusion-distributed-iceberg --locked — 95 tests passed, including the doctest
• cargo test -p datafusion-distributed-iceberg --features integration --locked — 96 tests passed, including the doctest
• cargo clippy -p datafusion-distributed-iceberg --all-targets --locked -- -D warnings — passed, also with --all-features
• cargo fmt --all -- --check
• git diff --check
• Mutation check: replacing computed column statistics with unknown values fails both enabled cases; the other eight tests pass. Mutation reverted.
• Bounds mutation check: keeping the first minimum and taking the last maximum fails both enabled cases; the other eight tests pass. Mutation reverted.

[alexanderbianchi]

Should we switch the harness to something more like

   async fn run(
       ctx: &SessionContext,
       query: &str,
   ) -> Result<(Arc<dyn ExecutionPlan>, Vec<RecordBatch>)> {
       let df = ctx.sql(query).await?;
       let plan = df.create_physical_plan().await?;
       let batches = collect(Arc::clone(&plan), ctx.task_ctx()).await?;
       Ok((plan, batches))
   }

from tests/multi_task_collect_join_repros.rs