rank() for int64 gives NAN for most negative value
#### Code to recreate the issue 

```python
import pandas as pd
import numpy as np
s = pd.Series(np.array([np.inf, np.nan, -np.inf]).astype(np.int32))
print(s)
print(s.rank())
s = pd.Series(np.array([np.inf, np.nan, -np.inf]).astype(np.int64))
print(s)
print(s.rank())
s = pd.Series([-9223372036854775808, -9223372036854775808, -9223372036854775808], dtype=np.int64)
print(s.rank())
```

```text
0   -2147483648
1   -2147483648
2   -2147483648
dtype: int32
0    2.0
1    2.0
2    2.0
dtype: float64
0   -9223372036854775808
1   -9223372036854775808
2   -9223372036854775808
dtype: int64
0   NaN
1   NaN
2   NaN
dtype: float64
0   NaN
1   NaN
2   NaN
dtype: float64
```
#### Problem description

rank for **`np.int64`** most negative value gives **NaN** as rank. 
`np.int32` works fine for similar case.

#### Expected Output
```text
0   -2147483648
1   -2147483648
2   -2147483648
dtype: int32
0    2.0
1    2.0
2    2.0
dtype: float64
0   -9223372036854775808
1   -9223372036854775808
2   -9223372036854775808
dtype: int64
0   2.0
1   2.0
2   2.0
dtype: float64
0   2.0
1   2.0
2   2.0
dtype: float64
```

#### Output of ``pd.show_versions()``

<details>


INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.9.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 1.0.2
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 41.6.0.post20191030
Cython           : 0.29.13
pytest           : 5.2.2
hypothesis       : None
sphinx           : 1.8.2
blosc            : None
feather          : None
xlsxwriter       : 1.2.2
lxml.etree       : 4.3.0
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : 4.8.1
bottleneck       : 1.2.1
fastparquet      : 0.1.6
gcsfs            : None
lxml.etree       : 4.3.0
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 2.5.12
pandas_gbq       : None
pyarrow          : 0.13.0
pytables         : None
pytest           : 5.2.2
pyxlsb           : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.10
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.2
numba            : 0.42.0
</details>
