[improve](nereids) filter nereidsPrunedTabletIds per partition in distributionPrune

[Larborator]

What problem does this PR solve?

#53403 short-circuited distributionPrune to return the entire nereidsPrunedTabletIds set when running under Nereids. However, the caller computeTabletInfo invokes distributionPrune inside a per-partition loop and then iterates the returned ids, calling MaterializedIndex.getTablet(id) on each. When nereidsPrunedTabletIds contains tablets across many
partitions, every per-partition iteration walks the entire global set and does a getTablet hash lookup on ids that belong to other partitions (which are then filtered out by the null check), yielding O(partitionNum * globalPrunedSize) lookups. The short-circuit also copies the full HashSet into a new ArrayList once per partition.

Filter the global set down to the current partition's tablet ids (tabletIdsInOrder, already prepared by the caller) before returning. The result is identical to what the caller's null-check would have produced, so behavior is unchanged; only the redundant lookups and copies are eliminated. The non-Nereids path, the sampleTabletIds path and the empty-set
fallback are untouched.

Issue Number: close #63854

Related PR: #53403

Problem Summary:

Plan time of OlapScan queries with many partitions and many globally pruned tablets degrades quadratically due to redundant per-partition iterations over the global pruned tablet set in OlapScanNode.distributionPrune. Restore per-partition complexity by filtering the global set down to the current partition's tablets before returning.

Release note

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Check List (For Author)

• Test

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  • Manual test (add detailed scripts or steps below)
  • No need to test or manual test. Explain why:
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• Behavior changed:

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  • Yes.

• Does this need documentation?

  • No.
  • Yes.

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[hello-stephen]

Thank you for your contribution to Apache Doris.
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Please clearly describe your PR:

1. What problem was fixed (it's best to include specific error reporting information). How it was fixed.
2. Which behaviors were modified. What was the previous behavior, what is it now, why was it modified, and what possible impacts might there be.
3. What features were added. Why was this function added?
4. Which code was refactored and why was this part of the code refactored?
5. Which functions were optimized and what is the difference before and after the optimization?

[Larborator]

/review