BUG: Binary op with datetime offset is resulting in incorrect results

[galipremsagar]

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

In [1]: import pandas as pd

In [2]: s = pd.Series([
   ...:             "2000-01-01 00:00:00.012345678",
   ...:             "2000-01-31 00:00:00.012345678",
   ...:             "2000-02-29 00:00:00.012345678",
   ...:         ], dtype='datetime64[ns]')

In [3]: s
Out[3]: 
0   2000-01-01 00:00:00.012345678
1   2000-01-31 00:00:00.012345678
2   2000-02-29 00:00:00.012345678
dtype: datetime64[ns]

In [4]: o = pd.DateOffset(milliseconds=4)

In [5]: o
Out[5]: <DateOffset: milliseconds=4>

In [6]: s + o
Out[6]: 
0   2000-01-01 00:00:00.012345678
1   2000-01-31 00:00:00.012345678
2   2000-02-29 00:00:00.012345678
dtype: datetime64[ns]

In [7]: o + s
Out[7]: 
0   2000-01-01 00:00:00.012345678
1   2000-01-31 00:00:00.012345678
2   2000-02-29 00:00:00.012345678
dtype: datetime64[ns]

Issue Description

The millisecond values are not being added.

Expected Behavior

(Pdb) o + s
0   2000-01-01 00:00:00.016345678
1   2000-01-31 00:00:00.016345678
2   2000-02-29 00:00:00.016345678
dtype: datetime64[ns]

Installed Versions

INSTALLED VERSIONS

commit : fd3f571
python : 3.10.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.15.0-94-generic
Version : #104-Ubuntu SMP Tue Jan 9 15:25:40 UTC 2024
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 2.2.0
numpy : 1.26.4
pytz : 2024.1
dateutil : 2.8.2
setuptools : 69.1.0
pip : 24.0
Cython : 3.0.8
pytest : 7.4.4
hypothesis : 6.98.9
sphinx : 6.2.1
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.3
IPython : 8.21.0
pandas_datareader : None
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : 4.12.3
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2024.2.0
gcsfs : None
matplotlib : None
numba : 0.59.0
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 14.0.1
pyreadstat : None
python-calamine : None
pyxlsb : None
s3fs : 2024.2.0
scipy : 1.12.0
sqlalchemy : 2.0.27
tables : None
tabulate : 0.9.0
xarray : None
xlrd : None
zstandard : None
tzdata : 2024.1
qtpy : None
pyqt5 : None

[galipremsagar]

cc: @mroeschke