feat(datafusion): Add opt-in eager file scan planning with output partitioning

[toutane]

Which issue does this PR close?

• Closes Enable parallel file-level scanning for IcebergTableScan Datafusion Integration #2220
• Related to
  • EPIC: Support parallel scan in iceberg-datafusion #1604
  • Plan file scan task according scan file size. #128
  • feat!(datafusion): enable parallel file scanning with eager task bucketing #2298

What changes are included in this PR?

This PR adds an opt-in eager scan planning path for the DataFusion integration.

When iceberg.enable_eager_scan_planning is enabled, IcebergTableProvider::scan() plans FileScanTasks during physical planning, groups them across DataFusion target_partitions, and exposes the resulting output partition count as UnknownPartitioning(N). Each execute(partition) call then reads only the task group assigned to that output partition through ArrowReaderBuilder.

The default behavior is unchanged: eager planning is disabled by default, so scans keep the existing lazy single-partition planning path unless explicitly enabled.

This is a narrower split from #2298, focused on file-level eager planning and output partition count reporting. Some of the broader design discussions and review feedback happened in #2298; this PR keeps only the first scoped step and intentionally does not include hash partitioning, size-aware bin-packing, or row-group/sub-file planning.

Trade-offs:

• Eager planning does catalog/metadata work during TableProvider::scan(), so it is kept opt-in
• Task grouping is round-robin and count-based, not size-aware. This is simple and deterministic, but can be imbalanced when file sizes vary
• Parallelism is at the FileScanTask level only. A table with one large file will not benefit from this change
• The scan reports UnknownPartitioning(N), not hash partitioning. This exposes the number of output partitions without claiming stronger partitioning semantics

Follow-up work:

• Add size-aware task bin-packing using file_size_in_bytes (Plan file scan task according scan file size. #128)
• Add sub-file / row-group level parallelism as the longer-term direction (EPIC: Support parallel scan in iceberg-datafusion #1604)
• Cache planned tasks for repeated scans with the same snapshot, projection, filter, and target partition count
• Investigate stronger output partitioning declarations for safe identity-partitioned cases
• Add benchmarks for multi-file tables and skewed file-size distributions

Are these changes tested?

Yes. Integration tests cover:

• eager scan planning disabled by default
• enabling eager scan planning through iceberg.enable_eager_scan_planning
• exposing multiple scan output partitions when eager planning is enabled
• preserving query results between single-partition and multi-partition scans