[Python] Allow `pyarrow.compute.cast` to coerce errors to null values

### Describe the enhancement requested

I have large array consisting of string data. Unfortunately, there is numerical data mixed with categorical data. `pyarrow` seems to offer no straightforward way to separate them.

```python
import pyarrow as pa

arr = pa.array(["3", "+5", "-4.2", "1,000.00", "foo", "7e-3"], type="string")
print(pa.compute.utf8_is_numeric(arr)) # ynnnnn
pa.compute.cast(arr, pa.float32(), safe=False) # ArrowInvalid: Failed to parse string: 'foo' as a scalar of type float
```

basically, it would be great to have either (or both)

- function that returns boolean mask whether string can be cast to float
- add option to `pyarrow.compute.cast` that replaces errors with null values.

My current workaround is to use cast to pandas: `pd.to_numeric(pd.Series(arr, dtype="string[pyarrow]"), errors="coerce")`.

### Component(s)

Python

Start with the pa.compute.cast entry point and reproduce the mixed string example from the issue. Compare the requested boolean-mask and error-to-null alternatives, then verify that the selected behavior preserves successful conversions while representing unparseable values as null.

Written by the indexing model from the issue text.