BUG: TimeDeltaIndex slicing with strings incorrectly includes too much data
- [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.

---

**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 numpy as np
import pandas as pd
fs = 50000
i = pd.to_timedelta(np.arange(900 * fs, dtype=np.int) / fs * 1e9, unit='ns')
df = pd.DataFrame({'dummy': np.arange(len(i))}, index=i)
assert np.isclose(len(df['710s':'711s']) / fs, 2.0)
assert np.isclose(len(df['710s':'719s']) / fs, 10.0)
assert np.isclose(len(df['610s':'620s']) / fs, 11.0)
assert np.isclose(len(df['710s':'720.00000001s']) / fs, 10.00002)
assert np.isclose(len(df['710s':'720s']) / fs, 11.0)  # fails! Slices 70 seconds of data??
```

#### Problem description
Slicing a dataframe with a TimeDeltaIndex with the particular right bound '720s' seems to be incorrectly parsed, not returning the time slice as expected. As can be seen in the above example, other bounds work as expected, but using '720s' as the right bound returns 60 more seconds of data than it should have.

#### Expected Output
Slicing between '710s' and '720s' should return 11 seconds of data, as slicing '610s' and '620s' does.

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

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.0.0-29-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.3
numpy            : 1.18.2
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 9.0.1
setuptools       : 46.1.3
Cython           : None
pytest           : 4.3.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : 0.999999999
pymysql          : 0.9.3
psycopg2         : None
jinja2           : 2.10.3
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.2.1
numexpr          : None
odfpy            : None
openpyxl         : 2.4.11
pandas_gbq       : None
pyarrow          : 0.13.0
pytables         : None
pytest           : 4.3.1
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : 1.3.12
tables           : None
tabulate         : 0.8.6
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : None
numba            : 0.48.0

</details>
