Accessing DataFrame with DatetimeIndex behaves unexpectedly with reverse chronological ordered data
#### Problem description

When a Panda's DataFrame with a DatetimeIndex is in reverse chronological order accessing the data using the .loc method does not work unless the range is _also_ in reverse order.

#### Code Sample

```python
import pandas as pd
 
data = [
  ("2020-03-25 12:00:00", "A"),
  ("2020-03-24 18:00:00", "B"),
  ("2020-03-24 12:00:00", "C"),
  ("2020-03-24 06:00:00", "D"),
  ("2020-03-23 12:00:00", "E"),
]
 
df = pd.DataFrame(data, columns=["timestamp", "x"])
df["timestamp"] = pd.to_datetime(df["timestamp"])
df.index = df.pop("timestamp")
```

Accessing the date range 2020-03-24 through 2020-03-25 for this DataFrame results in the following:

```python
df.loc["2020-03-24":"2020-03-25"]
>>>
Empty DataFrame
Columns: [x]
Index: []
```

However, reversing the date range returns the expected output.

```python 
df.loc["2020-03-25":"2020-03-24"]
>>>
                     x
timestamp            
2020-03-25 12:00:00  A
2020-03-24 18:00:00  B
2020-03-24 12:00:00  C
2020-03-24 06:00:00  D
```

Is this really how the DatetimeIndex should function? Either should work when working with a DatetimeIndex.

This can be fixed by executing the .sort_index() method of the DataFrame. However, when working with a DatetimeIndex, I believe chronological order should be implied.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.8.0.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-88-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: 5.4.1
pip: 20.0.2
setuptools: 41.2.0
Cython: None
numpy: 1.18.1
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: 2.4.4
patsy: None
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.2.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.8.2
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.11.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
