Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import numpy as np
import pandas as pd

s = pd.Series([10000000000000000000], dtype="uint64")
s.sum()  # -8446744073709551616
s.to_numpy().sum()  # 10000000000000000000

s = pd.Series([10000000000000000000], dtype="uint64[pyarrow]")
s.sum()  # 10000000000000000000

Issue Description

The summation of a series of uint64 is converted to int64.

Expected Behavior

The result would be a uint64.

Installed Versions

INSTALLED VERSIONS
------------------
commit           : 37ea63d540fd27274cad6585082c91b1283f963d
python           : 3.11.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 6.2.6-76060206-generic
Version          : #202303130630~1683753207~22.04~77c1465 SMP PREEMPT_DYNAMIC Wed M
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 2.0.1
numpy            : 1.24.3
pytz             : 2023.3
dateutil         : 2.8.2
setuptools       : 67.7.2
pip              : 23.1.2
Cython           : None
pytest           : 7.3.1
hypothesis       : None
sphinx           : 5.3.0
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : 4.9.2
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 3.1.2
IPython          : 8.13.2
pandas_datareader: None
bs4              : 4.12.2
bottleneck       : None
brotli           : 
fastparquet      : 2023.4.0
fsspec           : 2023.5.0
gcsfs            : None
matplotlib       : 3.7.1
numba            : 0.57.0
numexpr          : None
odfpy            : None
openpyxl         : 3.1.2
pandas_gbq       : None
pyarrow          : 11.0.0
pyreadstat       : None
pyxlsb           : None
s3fs             : None
scipy            : 1.10.1
snappy           : 
sqlalchemy       : 1.4.46
tables           : None
tabulate         : 0.9.0
xarray           : 2023.5.0
xlrd             : None
zstandard        : None
tzdata           : 2023.3
qtpy             : None
pyqt5            : None

Confirm the same issue on the "main" branch

Yes. this is a bug.

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