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- Convert irregular outputs with np.ma.masked sentinels to proper MaskedArrays in _output_from_mapspec_task
- Fix DictArray.__getitem__ to handle slicing of irregular arrays with proper padding
- Update tests to use explicit mapspecs instead of add_mapspec_axis (which generates invalid mapspecs for irregular dimensions)
- Fix test expectations to match actual auto-inference behavior
- Handle both MaskedArrays and object arrays with sentinel values in reduction functions

All tests in test_irregular_arrays.py now pass correctly.

- Add SparseIrregularArray class for memory-efficient storage of irregular arrays
- Integrate sparse array support into DictArray with auto-detection
- Auto-use sparse when total size >10k elements or density <10%
- Add comprehensive test coverage for sparse arrays (3 test files, 37 tests)
- Achieve 999x memory reduction for highly variable array sizes (e.g., 99 arrays of size 1 + 1 array of size 1000)

The sparse implementation provides an array-like interface while storing only actual data values, dramatically reducing memory usage when array sizes vary from 1 to billions of elements.

This reverts commit bac3c7f.

Replace inefficient masking implementation with vectorized approach using
np.frompyfunc. This significantly improves performance for irregular arrays:

- Previous: O(n) time/space with flatten, iterate, copy operations
- New: Vectorized sentinel detection with minimal memory overhead
- Simpler code: 7 lines vs 20+ lines

The new implementation correctly identifies np.ma.masked sentinels and
creates proper MaskedArrays while being more efficient.

- Add create_mask_for_masked_values() helper to _utils.py
- Add _ensure_masked_array_for_irregular() to StorageBase
- Update DictArray.__getitem__ to return MaskedArrays for irregular data
- Add comprehensive tests for irregular array sum operations

When irregular arrays contain np.ma.masked sentinel values, they are now
automatically wrapped in MaskedArrays with proper masks, providing a cleaner
API for users to handle varying-length data.