BUG: datetime total_seconds inaccurate
- [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.

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**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.

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

```python
import pandas as pd

df = pd.DataFrame({'x':['2020-07-07T10:33:14.971260986',
       '2020-07-07T10:17:14.971260986', '2020-07-07T10:11:14.971260986',
       '2020-07-07T09:59:13.971260986'],'y':['2020-07-07T10:35:14.971260986',
       '2020-07-07T10:24:14.971260986', '2020-07-07T10:15:14.971260986',
       '2020-07-07T10:04:14.971260986']})

df.x = pd.to_datetime(df.x)
df.y = pd.to_datetime(df.y)

print((df.y-df.x).dt.total_seconds()[0]) # 120.00000000000001
print((df.y-df.x).apply(lambda x: x.total_seconds())[0]) #120.0

```

#### Problem description

There seems to be a loss of precision when doing date substractions and using total_seconds method.

As a reference, using an apply with the standard library total_seconds yields the correct value.

#### Expected Output

Value should be 120.0
#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.5.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.0.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.UTF-8

pandas           : 1.0.5
numpy            : 1.17.4
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 42.0.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.14.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.11
tables           : None
tabulate         : 0.8.6
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
</details>
