[improvement](parquet) Fuse dictionary selection planning

[Gabriel39]

What problem does this PR solve?

Predicate-only dictionary filtering currently expands definition levels and row selection into ColumnSelectVector, then scans that row-oriented state again to rebuild physical selection ranges and the selected NULL layout. Fragmented nullable filters spend significant CPU in these two planning passes before dictionary IDs are decoded.

Same-column conjuncts are combined into one dictionary-id bitmap before row decoding. Their incoming row selection therefore remains identity, which previously kept this common shape on the two-pass planner.

What is changed?

• Build physical dictionary selection ranges, selected NULL layout, and the filtered-row count directly while merging definition-level runs with the input filter.
• Pass the prepared plan into dictionary-index filtering instead of expanding and rescanning ColumnSelectVector.
• Plan identity-input dictionary selections directly when definition levels are materially fragmented.
• Use a bounded run-search fast path for no-NULL clustered filters, then switch to one linear scan after 32 runs.
• Keep small batches, dense nullable inputs, compact identity inputs, and sparse clustered nullable inputs on the existing planner.
• Add a self-validating 220-case dictionary-selection benchmark matrix, including 20 identity-input cases.

This PR does not change predicate decomposition, supported data types, decode strategy, or merge-read behavior. It is stacked on #66504.

Performance

Release build, 65,536 rows, CPU-pinned runs with 7 repetitions. The table reports median CPU time; lower is better.

Input selection	NULL layout	NULL rate	Existing planner	Selected planner	Change
Identity	Alternating	1%	11,066 ns	8,383 ns	-24.24%
Identity	Alternating	10%	98,943 ns	60,185 ns	-39.17%
Identity	Alternating	50%	580,955 ns	319,267 ns	-45.04%
Identity	Alternating	90%	87,733 ns	56,741 ns	-35.33%
Identity	No NULL	0%	1,451 ns	1,458 ns	+0.47%
Identity	Clustered	1%	1,488 ns	1,497 ns	+0.59%

The last two negative controls are routed to the existing planner; their sub-1% differences are measurement noise from identical code paths.

Validation

• 7 targeted ASAN Parquet dictionary and nullable-selection tests passed.
• All 220 dictionary-selection scenarios completed and self-validated identical physical ranges, selected NULL layout, and filtered-row counts.
• All affected C/C++ files pass clang-format 16.

[hello-stephen]

Thank you for your contribution to Apache Doris.
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1. What problem was fixed (it's best to include specific error reporting information). How it was fixed.
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[Gabriel39]

run buildall

[Gabriel39]

/review

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[Gabriel39]

run buildall

[Gabriel39]

/review

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