branch-4.0 [opt](fe) Bound not-null inference cost (#63318)

[englefly]

What problem does this PR solve?

pick #63318
Issue Number: close #xxx

Related PR: #xxx

Problem Summary:
Not-null inference replaces candidate slots with NULL and folds each predicate, so wide, deep, or high-slot expressions can make rewrite rules spend excessive time in repeated replace and fold work. Aggregate not-null inference also needed to handle multiple aggregate outputs conservatively instead of inferring from all aggregate arguments as one set.

This change adds a shared bounded guard for not-null inference, reuses it from aggregate inference, and lets general callers skip only expensive predicates while preserving the original query predicates. It also reworks join inference to compute nullable-rejecting slots once and reuse them for both sides, and makes aggregate inference require a common inferred not-null predicate across supported
aggregate functions.

optimized rule:
InferAggNotNull、InferFilterNotNull、InferJoinNotNull、EliminateNotNull

What problem does this PR solve?

Issue Number: close #xxx

Related PR: #xxx

Problem Summary:

Release note

None

Check List (For Author)

• Test

  • Regression test
  • Unit Test
  • Manual test (add detailed scripts or steps below)
  • No need to test or manual test. Explain why:
    • This is a refactor/code format and no logic has been changed.
    • Previous test can cover this change.
    • No code files have been changed.
    • Other reason

• Behavior changed:

  • No.
  • Yes.

• Does this need documentation?

  • No.
  • Yes.

Check List (For Reviewer who merge this PR)

• Confirm the release note
• Confirm test cases
• Confirm document
• Add branch pick label

[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?

[englefly]

run buildall