fix: compare ScalarValue maps using Arrow ordering

[viirya]

Which issue does this PR close?

Close #25556

Rationale for this change

ScalarValue::partial_cmp only compared Map keys and ignored their values. Maps with identical keys but different values were therefore considered equal.

This could produce incorrect ordering when merging ordered aggregate states, such as first_value, last_value, and nth_value, and could incorrectly validate range partition split points.

What changes are included in this PR?

Use Arrow's Map comparator for ScalarValue::Map comparisons. This aligns scalar comparison with Arrow sorting and nested comparison semantics, including values, entry order, lengths, and nulls.

Add unit tests covering these cases.

What is the testing strategy for this PR?

Added test_map_partial_cmp covering:

• maps with identical keys and different values
• lexicographic (key, value) entry ordering
• maps of different lengths
• null and empty maps

Are there any user-facing changes?

Map scalar comparisons now consider both keys and values and are consistent with Arrow ordering.

[codecov-commenter]

Codecov Report

❌ Patch coverage is 96.42857% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 82.41%. Comparing base (710c748) to head (d51a627).
⚠️ Report is 21 commits behind head on main.

Files with missing lines	Patch %	Lines
datafusion/common/src/scalar/mod.rs	96.42%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main   #25555      +/-   ##
==========================================
+ Coverage   82.38%   82.41%   +0.03%     
==========================================
  Files        1138     1138              
  Lines      434313   435420    +1107     
  Branches   434313   435420    +1107     
==========================================
+ Hits       357805   358872    +1067     
+ Misses      54876    54845      -31     
- Partials    21632    21703      +71     

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[kumarUjjawal]

Thank you @viirya

Looks good 👍

[viirya]

Thank you @kumarUjjawal !