ENH: Make maybe_convert_object respect dtype itemsize
- [x] Ensure all linting tests pass, see [here](https://pandas.pydata.org/pandas-docs/dev/development/contributing.html#code-standards) for how to run them

Precursor to #40790

This adds support for e.g. `float32` NumPy dtypes to maybe_convert_object. If any non-NumPy scalar is hit, the behavior is the same as master. This is my first foray into the NumPy C-API, so any tips are appreciated. In particular, I couldn't figure out how to use the C API to do the cast:

    result = result.astype(result.dtype.kind + str(itemsize))

Not sure if there should also be specific logic for EAs/nullable types.

From a full ASV run:

```
       before           after         ratio
     [7d4757b4]       [c1288962]
     <maybe_convert_object_itemsize~12>       <maybe_convert_object_itemsize>
+         768±8μs         1.01±0ms     1.32  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'float')
+         779±9μs         1.02±0ms     1.31  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'float')
+         579±5μs         710±20μs     1.23  arithmetic.NumericInferOps.time_divide(<class 'numpy.int8'>)
+        608±30μs         715±20μs     1.18  arithmetic.NumericInferOps.time_divide(<class 'numpy.uint8'>)
+      3.18±0.2μs       3.69±0.3μs     1.16  index_cached_properties.IndexCache.time_engine('UInt64Index')
+     1.58±0.01ms      1.83±0.01ms     1.16  ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f5964729820>, False, 'float')
+      3.30±0.2μs       3.80±0.5μs     1.15  index_cached_properties.IndexCache.time_engine('TimedeltaIndex')
+        943±80ns       1.08±0.1μs     1.15  index_cached_properties.IndexCache.time_inferred_type('Float64Index')
+     1.73±0.01ms         1.98±0ms     1.14  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'int')
+     1.72±0.01ms         1.96±0ms     1.14  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'int')
+     5.19±0.03μs       5.93±0.3μs     1.14  tslibs.period.TimeDT64ArrToPeriodArr.time_dt64arr_to_periodarr(1, 6000, datetime.timezone(datetime.timedelta(seconds=3600)))
+     1.65±0.01ms      1.89±0.01ms     1.14  ctors.SeriesConstructors.time_series_constructor(<function arr_dict at 0x7f5964729820>, True, 'float')
+     6.06±0.06μs       6.91±0.1μs     1.14  tslibs.tz_convert.TimeTZConvert.time_tz_convert_from_utc(1, datetime.timezone(datetime.timedelta(seconds=3600)))
+     7.46±0.04μs       8.47±0.5μs     1.14  tslibs.offsets.OffestDatetimeArithmetic.time_subtract(<BusinessDay>)
+     1.15±0.09μs      1.27±0.08μs     1.11  index_cached_properties.IndexCache.time_values('UInt64Index')
-        11.7±1ms       10.5±0.1ms     0.90  algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.object_'>, 18, 'outside')
-        641±30ns         577±20ns     0.90  index_cached_properties.IndexCache.time_is_monotonic('RangeIndex')
-        641±20ns         576±20ns     0.90  index_cached_properties.IndexCache.time_shape('Int64Index')
-     3.37±0.03ms      3.02±0.02ms     0.90  timeseries.ResampleSeries.time_resample('period', '5min', 'ohlc')
-        787±20ns         702±20ns     0.89  index_cached_properties.IndexCache.time_is_monotonic_decreasing('RangeIndex')
-     1.25±0.04μs      1.12±0.04μs     0.89  index_cached_properties.IndexCache.time_is_all_dates('Int64Index')
-      1.50±0.1ms      1.33±0.02ms     0.88  dtypes.SelectDtypes.time_select_dtype_string_exclude('float32')
-     1.19±0.09μs      1.05±0.08μs     0.88  index_cached_properties.IndexCache.time_inferred_type('UInt64Index')
-        57.6±1μs       50.3±0.2μs     0.87  frame_methods.Dtypes.time_frame_dtypes
-         168±3μs        146±0.6μs     0.87  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.uint64'>, 8000)
-        552±20ns         480±20ns     0.87  index_cached_properties.IndexCache.time_is_monotonic_increasing('RangeIndex')
-      1.50±0.1ms         1.30±0ms     0.87  dtypes.SelectDtypes.time_select_dtype_string_exclude('complex64')
-        1.89±0ms      1.64±0.01ms     0.87  period.DataFramePeriodColumn.time_set_index
-      11.0±0.2μs      9.50±0.08μs     0.87  period.Indexing.time_series_loc
-        110±20ms      93.1±0.06ms     0.85  algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'random_hits')
-        392±30ns         330±10ns     0.84  index_cached_properties.IndexCache.time_inferred_type('RangeIndex')
-      4.45±0.4ms      3.68±0.02ms     0.83  algorithms.Factorize.time_factorize(True, True, 'Int64')
-      9.60±0.8ms      7.75±0.01ms     0.81  algorithms.Factorize.time_factorize(True, False, 'string')
-      11.2±0.8ms       8.90±0.1ms     0.80  algos.isin.IsinAlmostFullWithRandomInt.time_isin(<class 'numpy.object_'>, 18, 'inside')
-        615±30ns         484±30ns     0.79  index_cached_properties.IndexCache.time_is_monotonic_increasing('Int64Index')
-       73.8±20ms       55.4±0.5ms     0.75  algos.isin.IsInLongSeriesLookUpDominates.time_isin('float32', 1000, 'monotone_hits')
-       180±0.1μs        128±0.5μs     0.71  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int8Engine'>, <class 'numpy.int8'>), 'monotonic_incr')
-        128±40ms      90.8±0.03ms     0.71  algos.isin.IsInLongSeriesLookUpDominates.time_isin('object', 5, 'monotone_hits')
-       186±0.3μs          131±2μs     0.70  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.Int16Engine'>, <class 'numpy.int16'>), 'monotonic_incr')
-      1.72±0.4μs       1.17±0.1μs     0.68  index_cached_properties.IndexCache.time_inferred_type('TimedeltaIndex')
-       216±0.8μs          140±3μs     0.65  indexing_engines.NumericEngineIndexing.time_get_loc((<class 'pandas._libs.index.UInt32Engine'>, <class 'numpy.uint32'>), 'monotonic_incr')
-     1.55±0.01ms          972±7μs     0.63  algos.isin.IsinWithArangeSorted.time_isin(<class 'numpy.int64'>, 100000)

SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.
PERFORMANCE DECREASED.
```

Specific timings via %timeit on `maybe_convert_objects` directly:

```
np.array of python integers:
127 ms ± 80.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)  <--- PR
120 ms ± 155 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)   <--- master

np.array of int32:
179 ms ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)  <--- PR
133 ms ± 274 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)    <--- master

np.array of int32 with last one a python int:
178 ms ± 1.71 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)  <--- PR
133 ms ± 106 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)    <--- master
```

<details>
<summary>timeit code</summary>

```
values = np.array(range(1_000_000), dtype="object")
print('np.array of python integers:')
%timeit maybe_convert_objects(values)
print()

values = np.array([np.int32(1)] * 1_000_000, dtype="object")
print('np.array of int32:')
%timeit maybe_convert_objects(values)
print()

values = np.array([np.int32(1)] * 999_999 + [1], dtype="object")
print('np.array of int32 with last one a python int:')
%timeit maybe_convert_objects(values)
print()
```

<details>