BUG: grouping with tz-aware: Values falls after last bin
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

index = pd.DatetimeIndex([1478064900001000000, 1480037118776792000], tz='UTC').tz_convert('America/Chicago')

print(index)

df = pd.DataFrame([1, 2], index=index)

for d, data in df.groupby(pd.Grouper(freq='1d')):
    pass

```
#### Problem description
grouping is not handling non-UTC index properly due to daylight saving time change
and the problem occurs in file https://github.com/pandas-dev/pandas/blob/master/pandas/core/resample.py because of timestamp normalization line: 1638
```python
last = last.normalize()
```
the normalization causes an error, and I would expect pandas to simply add an extra bin, instead of raising an exception.
it was introduced in the latest version of pandas 0.24.0 and it does not reproduce on 0.23.4
[this should explain **why** the current behaviour is a problem and why the expected output is a better solution.]

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#### Expected Output
I expect the grouping to be successful regardless of the time range selected
#### Output of ``pd.show_versions()``

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.24.0
pytest: 4.1.1
pip: 18.1
setuptools: 40.6.3
Cython: None
numpy: 1.16.0
scipy: 1.2.0
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.5
pytz: 2018.9
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 3.0.2
openpyxl: 2.5.14
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: 0.9.3
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None
</details>
