Improve performance of series.ext_array.replace_with_mask()

Nobody has claimed this yet.

Currently, arrow misses the support of `pyarrow.compute.replace_with_mask` for struct arrays:
https://github.com/apache/arrow/issues/29558

That's why we have our own implementation used by `NestedExtenstionArray.__setitem__()`. The implementation has an overhead of creating a `len(self)`-sized struct array to perform the replacement. This approach would work well when we are going to replace many elements, but when we replacing just few, it would produce a large memory foot-print and probably take a while.

An alternative approach would be copying the original array to `np.ndarray[pa.StructScalar]`, replace the elements in-place, and convert it back:
```python
def replace_with_mask(array: pa.ChunkedArray, mask: pa.BooleanArray, value: pa.Array) -> pa.ChunkedArray:
"""Replace the elements of the array with the value where the mask is True"""
np_array = np.fromiter(array, dtype=object)
np_array[mask] = value
new_pa_array = pa.array(np_array)
return pa.chunked_array([new_pa_array])
```

We should create a benchmark and see what works faster and have smaller memory foot-print.

Start at series.ext_array.replace_with_mask() and its use by NestedExtenstionArray.__setitem__(). Benchmark the current struct-array implementation against the proposed NumPy-based approach for replacing many and few elements, measuring runtime and memory footprint. Done means the benchmark identifies which approach performs better for each case and supports an implementation decision.

Written by the indexing model from the issue text.