`test_maybe_promote_int_with_int` error on 32bit platform
The test `test_maybe_promote_int_with_int` in `tests/dtypes/cast/test_promote.py` is failing for a subset of combinations where `int8`, `uint8`, `int16`, or `uint16` values are promotoed to `int32` or `uint32` targets on i586 the platform (but not on x86_64).  The error message is pretty consistent.  

This failure is new in pandas 1.0.1, it was not present on 1.0.0.

The specific combination of values that fail are:

```
int16-32768-int32
int8-32768-int32
uint16--1-int32
uint16--129-int32
uint16-2147483648-uint32
uint16-65536-uint32
uint8-2147483648-uint32
uint8-65536-uint32
```

These do *not* fail:

```
int16--32769-int32
int8--32769-int32
uint16--32769-int32
uint8--32769-int32
```

A specific example of the error message is:

```
______________ test_maybe_promote_int_with_int[uint16--129-int32] ______________
linux -- Python 3.7.3 /usr/bin/python3

dtype = dtype('uint16'), fill_value = -129, expected_dtype = dtype('int32')

    @pytest.mark.parametrize(
        "dtype, fill_value, expected_dtype",
        [
            # size 8
            ("int8", 1, "int8"),
            ("int8", np.iinfo("int8").max + 1, "int16"),
            ("int8", np.iinfo("int16").max + 1, "int32"),
            ("int8", np.iinfo("int32").max + 1, "int64"),
            ("int8", np.iinfo("int64").max + 1, "object"),
            ("int8", -1, "int8"),
            ("int8", np.iinfo("int8").min - 1, "int16"),
            ("int8", np.iinfo("int16").min - 1, "int32"),
            ("int8", np.iinfo("int32").min - 1, "int64"),
            ("int8", np.iinfo("int64").min - 1, "object"),
            # keep signed-ness as long as possible
            ("uint8", 1, "uint8"),
            ("uint8", np.iinfo("int8").max + 1, "uint8"),
            ("uint8", np.iinfo("uint8").max + 1, "uint16"),
            ("uint8", np.iinfo("int16").max + 1, "uint16"),
            ("uint8", np.iinfo("uint16").max + 1, "uint32"),
            ("uint8", np.iinfo("int32").max + 1, "uint32"),
            ("uint8", np.iinfo("uint32").max + 1, "uint64"),
            ("uint8", np.iinfo("int64").max + 1, "uint64"),
            ("uint8", np.iinfo("uint64").max + 1, "object"),
            # max of uint8 cannot be contained in int8
            ("uint8", -1, "int16"),
            ("uint8", np.iinfo("int8").min - 1, "int16"),
            ("uint8", np.iinfo("int16").min - 1, "int32"),
            ("uint8", np.iinfo("int32").min - 1, "int64"),
            ("uint8", np.iinfo("int64").min - 1, "object"),
            # size 16
            ("int16", 1, "int16"),
            ("int16", np.iinfo("int8").max + 1, "int16"),
            ("int16", np.iinfo("int16").max + 1, "int32"),
            ("int16", np.iinfo("int32").max + 1, "int64"),
            ("int16", np.iinfo("int64").max + 1, "object"),
            ("int16", -1, "int16"),
            ("int16", np.iinfo("int8").min - 1, "int16"),
            ("int16", np.iinfo("int16").min - 1, "int32"),
            ("int16", np.iinfo("int32").min - 1, "int64"),
            ("int16", np.iinfo("int64").min - 1, "object"),
            ("uint16", 1, "uint16"),
            ("uint16", np.iinfo("int8").max + 1, "uint16"),
            ("uint16", np.iinfo("uint8").max + 1, "uint16"),
            ("uint16", np.iinfo("int16").max + 1, "uint16"),
            ("uint16", np.iinfo("uint16").max + 1, "uint32"),
            ("uint16", np.iinfo("int32").max + 1, "uint32"),
            ("uint16", np.iinfo("uint32").max + 1, "uint64"),
            ("uint16", np.iinfo("int64").max + 1, "uint64"),
            ("uint16", np.iinfo("uint64").max + 1, "object"),
            ("uint16", -1, "int32"),
            ("uint16", np.iinfo("int8").min - 1, "int32"),
            ("uint16", np.iinfo("int16").min - 1, "int32"),
            ("uint16", np.iinfo("int32").min - 1, "int64"),
            ("uint16", np.iinfo("int64").min - 1, "object"),
            # size 32
            ("int32", 1, "int32"),
            ("int32", np.iinfo("int8").max + 1, "int32"),
            ("int32", np.iinfo("int16").max + 1, "int32"),
            ("int32", np.iinfo("int32").max + 1, "int64"),
            ("int32", np.iinfo("int64").max + 1, "object"),
            ("int32", -1, "int32"),
            ("int32", np.iinfo("int8").min - 1, "int32"),
            ("int32", np.iinfo("int16").min - 1, "int32"),
            ("int32", np.iinfo("int32").min - 1, "int64"),
            ("int32", np.iinfo("int64").min - 1, "object"),
            ("uint32", 1, "uint32"),
            ("uint32", np.iinfo("int8").max + 1, "uint32"),
            ("uint32", np.iinfo("uint8").max + 1, "uint32"),
            ("uint32", np.iinfo("int16").max + 1, "uint32"),
            ("uint32", np.iinfo("uint16").max + 1, "uint32"),
            ("uint32", np.iinfo("int32").max + 1, "uint32"),
            ("uint32", np.iinfo("uint32").max + 1, "uint64"),
            ("uint32", np.iinfo("int64").max + 1, "uint64"),
            ("uint32", np.iinfo("uint64").max + 1, "object"),
            ("uint32", -1, "int64"),
            ("uint32", np.iinfo("int8").min - 1, "int64"),
            ("uint32", np.iinfo("int16").min - 1, "int64"),
            ("uint32", np.iinfo("int32").min - 1, "int64"),
            ("uint32", np.iinfo("int64").min - 1, "object"),
            # size 64
            ("int64", 1, "int64"),
            ("int64", np.iinfo("int8").max + 1, "int64"),
            ("int64", np.iinfo("int16").max + 1, "int64"),
            ("int64", np.iinfo("int32").max + 1, "int64"),
            ("int64", np.iinfo("int64").max + 1, "object"),
            ("int64", -1, "int64"),
            ("int64", np.iinfo("int8").min - 1, "int64"),
            ("int64", np.iinfo("int16").min - 1, "int64"),
            ("int64", np.iinfo("int32").min - 1, "int64"),
            ("int64", np.iinfo("int64").min - 1, "object"),
            ("uint64", 1, "uint64"),
            ("uint64", np.iinfo("int8").max + 1, "uint64"),
            ("uint64", np.iinfo("uint8").max + 1, "uint64"),
            ("uint64", np.iinfo("int16").max + 1, "uint64"),
            ("uint64", np.iinfo("uint16").max + 1, "uint64"),
            ("uint64", np.iinfo("int32").max + 1, "uint64"),
            ("uint64", np.iinfo("uint32").max + 1, "uint64"),
            ("uint64", np.iinfo("int64").max + 1, "uint64"),
            ("uint64", np.iinfo("uint64").max + 1, "object"),
            ("uint64", -1, "object"),
            ("uint64", np.iinfo("int8").min - 1, "object"),
            ("uint64", np.iinfo("int16").min - 1, "object"),
            ("uint64", np.iinfo("int32").min - 1, "object"),
            ("uint64", np.iinfo("int64").min - 1, "object"),
        ],
    )
    def test_maybe_promote_int_with_int(dtype, fill_value, expected_dtype):
        dtype = np.dtype(dtype)
        expected_dtype = np.dtype(expected_dtype)
    
        # output is not a generic int, but corresponds to expected_dtype
        exp_val_for_scalar = np.array([fill_value], dtype=expected_dtype)[0]
    
>       _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)

../../BUILDROOT/python-pandas-1.0.1-58.1.i386/usr/lib/python3.7/site-packages/pandas/tests/dtypes/cast/test_promote.py:237: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
../../BUILDROOT/python-pandas-1.0.1-58.1.i386/usr/lib/python3.7/site-packages/pandas/tests/dtypes/cast/test_promote.py:94: in _check_promote
    _assert_match(result_fill_value, expected_fill_value)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

result_fill_value = -129, expected_fill_value = -129

    def _assert_match(result_fill_value, expected_fill_value):
        # GH#23982/25425 require the same type in addition to equality/NA-ness
        res_type = type(result_fill_value)
        ex_type = type(expected_fill_value)
        if res_type.__name__ == "uint64":
            # No idea why, but these (sometimes) do not compare as equal
            assert ex_type.__name__ == "uint64"
        elif res_type.__name__ == "ulonglong":
            # On some builds we get this instead of np.uint64
            # Note: cant check res_type.dtype.itemsize directly on numpy 1.18
            assert res_type(0).itemsize == 8
            assert ex_type == res_type or ex_type == np.uint64
        else:
            # On some builds, type comparison fails, e.g. np.int32 != np.int32
>           assert res_type == ex_type or res_type.__name__ == ex_type.__name__
E           AssertionError: assert (<class 'numpy.intc'> == <class 'numpy.int32'>
E             -<class 'numpy.intc'>
E             +<class 'numpy.int32'> or 'intc' == 'int32'
E             - intc
E             + int32)
```

The error message is consistent, except that `intc` and `int32` are replaced with `uintc` and `uint32` in some cases.  I can provide all error messages if desired.

The tests are being run with numpy 1.18.1.