groupby with daily frequency fails with AmbiguousTimeError on clock change day in Cuba
#### Code Sample
```python
import pandas as pd
from datetime import datetime
start = datetime(2018, 11, 3, 12)
end = datetime(2018, 11, 5, 12)
index = pd.date_range(start, end, freq="1H")
index = index.tz_localize('UTC').tz_convert('America/Havana')
data = list(range(len(index)))
dataframe = pd.DataFrame(data, index=index)
groups = dataframe.groupby(pd.Grouper(freq='1D'))
```
#### Problem description
On a long clock-change day in Cuba, e.g 2018-11-04, midnight local time is an ambiguous timestamp. pd.Grouper does not handle this as I expect. More precisely the call to `groupby` in the code above raises an `AmbiguousTimeError`.

This issue is of a similar nature to https://github.com/pandas-dev/pandas/issues/23742 but it seems #23742 was fixed in 0.24 whereas this was not.

#### Expected Output
The call to `groupby` should return three groups (one for each day, 3rd, 4th, and 5th of november). The group for the 4th of november should be labelled as '2018-11-04 00:00:00-04:00' (that is the first midnight, before the clock change) and it should contain the 25 hourly data points for this day.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.125-linuxkit
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.2
pytest: 3.3.2
pip: None
setuptools: 40.6.3
Cython: 0.29.6
numpy: 1.15.4
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
