BUG: Sum on large integers incorrect after rolling timewindow
- [x] I have checked that this issue has not already been reported.

- [x] I have confirmed this bug exists on the latest version of pandas.

- [ ] (optional) I have confirmed this bug exists on the master branch of pandas.

---

#### Code Sample, a copy-pastable example

```python
import pandas as pd
from datetime import datetime, timedelta

s1 = pd.Series([138, 2377900603251717750], index=pd.Index([datetime(year=1970, month=1, day=1), datetime(year=1970, month=1, day=1)+timedelta(microseconds=1)]))
s2 = s1.rolling("1D").sum()
print(s2.sum() >= s1.sum())
```

#### Problem description
The sum of the rolling window should be higher or equal to the original series because only positive values are in it. However, 'False' is printed.

#### Expected Output
True

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

<details>
INSTALLED VERSIONS
------------------
commit           : 7d32926db8f7541c356066dcadabf854487738de
python           : 3.7.10.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 20.3.0
Version          : Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.UTF-8
pandas           : 1.2.2
numpy            : 1.19.4
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 49.6.0.post20210108
Cython           : None
pytest           : 6.2.2
hypothesis       : 6.3.1
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.5.2
html5lib         : None
pymysql          : 1.0.2
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.20.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : None
fsspec           : None
fastparquet      : None
gcsfs            : None
matplotlib       : 3.2.2
numexpr          : None
odfpy            : None
openpyxl         : 3.0.6
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.6.0
sqlalchemy       : 1.3.23
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
