PERF: improve construct_1d_object_array_from_listlike
This improved `construct_1d_object_array_from_listlike`, especially for the case where the objects inside the array like are itself array-likes with a potentially expensive conversion to numpy. 
It seems that when doing `result[:] = values`, numpy will still check the `__array__` method for each object in `values`, while when iterating and assigning the objects one by one, that does not happen. 

And even in the case where `__array__` is not expensive at all (or is absent), it seems that iterating is faster than the single assignment:

```
In [12]: class A:
    ...:     def __init__(self):
    ...:         self.data = np.random.randn(5)
    ...:     def __array__(self, dtype=None, copy=None):
    ...:         #print("calling __array__")
    ...:         return self.data

In [13]: N = 10_000

In [14]: data = [A() for _ in range(N)]

In [17]: %%timeit
    ...: arr = np.empty((N, ), dtype=object)
    ...: arr[:] = data
    ...: 
5.39 ms ± 33.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [18]: %%timeit
    ...: arr = np.empty((N, ), dtype=object)
    ...: for i, obj in enumerate(data):
    ...:     arr[i] = obj
    ...: 
424 µs ± 6.78 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)
```

This is a useful performance improvement in general, I assume, but I am specifically doing it to fix the performance issue reported in https://github.com/pandas-dev/pandas/issues/59657. That does is mostly for the 2.3.x branch, because that issue is avoided on main because of https://github.com/pandas-dev/pandas/pull/57205 (avoid Series construction, which ends up calling `construct_1d_object_array_from_listlike`, in the first place)


- [ ] closes #59657
- [ ] [Tests added and passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#writing-tests) if fixing a bug or adding a new feature
- [ ] All [code checks passed](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#pre-commit).
- [ ] Added [type annotations](https://pandas.pydata.org/pandas-docs/dev/development/contributing_codebase.html#type-hints) to new arguments/methods/functions.
- [ ] Added an entry in the latest `doc/source/whatsnew/vX.X.X.rst` file if fixing a bug or adding a new feature.



Some mypy errors, but nice find!
```bash
mypy.....................................................................Failed
- hook id: mypy
- duration: 88.21s
- exit code: 1

pandas/core/dtypes/cast.py:1604: error: Argument 1 to "len" has incompatible type "Iterable[Any]"; expected "Sized"  [arg-type]
pandas/core/common.py:256: error: Unused "type: ignore" comment  [unused-ignore]
Found 2 errors in 2 files (checked 1446 source files)
``` > Some mypy errors,

If I want something that is both Iterable and Sized, then that's Collection or Sequence ? > If I want something that is both Iterable and Sized, then that's Collection or Sequence ?

Appears either should work from the inheritance structure (Sequence inherits from Collection) https://docs.python.org/3/library/collections.abc.html#collections-abstract-base-classes Thanks @jorisvandenbossche 