[I] Efficient way to iterate over groups [arrow]

[via GitHub]

MarcoGorelli opened a new issue, #44676:
URL: https://github.com/apache/arrow/issues/44676

   ### Describe the enhancement requested
   
   In pandas / Polars, I can do:
   ```python
   dict(df.group_by(['a', 'b', 'c']).__iter__())
   ```
   
   There doesn't seem to be a built-in way to do this in PyArrow, hence I'm 
opening this as a feature request
   
   Concretely, if I have
   ```python
   import pyarrow as pa
   tbl = pa.table({'a': [1,1,3], 'b': [4, 4, 4], 'c': [1, 3, 2]})
   ```
   then I'd like a way to end up with
   ```
   {(3,
     4): pyarrow.Table
    a: int64
    b: int64
    c: int64
    ----
    a: [[3]]
    b: [[4]]
    c: [[2]],
    (1,
     4): pyarrow.Table
    a: int64
    b: int64
    c: int64
    ----
    a: [[1,1]]
    b: [[4,4]]
    c: [[1,3]]}
   ```
   
   ---
   
   For context, this would be for use in [Narwhals](), where we have tried to 
come up with a 
[workaround](https://github.com/narwhals-dev/narwhals/blob/9ecd1767d2cea6956d9ca8b2a12d584705f8023a/narwhals/_arrow/group_by.py#L82-L92),
 but it does exhibit a noticeable slow-down as the number of grouping keys 
grows - 3 keys is enough for it to be slower than pandas
   
   ### Component(s)
   
   Python


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