PERF: 10x speedup in Series/DataFrame construction for lists of ints
This PR is a minor tweak to the `int64`/`uint64` overflow fix added in https://github.com/pandas-dev/pandas/pull/18624 

Simply casting to an `int` after doing a typecheck is sufficient for the compiler to generate a 10x speedup:
```
$ asv compare upstream/master HEAD --sort ratio -s

Benchmarks that have improved:

       before           after         ratio
     [f074abef]       [80641ddf]
     <series_list_int_speedup~1>       <series_list_int_speedup>
           failed          7.39±0s      n/a  strings.Dummies.time_get_dummies
-        61.7±3ms       11.7±0.5ms     0.19  ctors.SeriesConstructors.time_series_constructor(<function arr_dict>, True, 'int')
-        63.0±2ms       11.1±0.3ms     0.18  ctors.SeriesConstructors.time_series_constructor(<function arr_dict>, False, 'int')
-        55.8±2ms       5.37±0.2ms     0.10  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, True, 'int')
-        55.3±5ms       4.84±0.2ms     0.09  ctors.SeriesConstructors.time_series_constructor(<class 'list'>, False, 'int')
```
This is how `maybe_convert_numeric()` already handles `int`s, so this just brings `maybe_convert_object()` back into alignment.

I believe this would yield a similar speedup for `DataFrame`s but we don't have any benchmarks explicitly testing as such. However, the `get_dummies()` benchmark involves expanding to a `DataFrame` and gets a speedup of similar magnitude (not visible as it previously would time out after 30s).

- [ ] closes #xxxx
- [ ] tests added / passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [ ] whatsnew entry
