DateTimeIndex.__iter__().next() rounds time to microseconds, when timezone aware
#### Code Sample

```python
>> import pandas as pd
>> datetimeindex = pd.DatetimeIndex(["2018-02-08 15:00:00.168456358"])
>> datetimeindex
DatetimeIndex(['2018-02-08 15:00:00.168456358'], dtype='datetime64[ns]', freq=None)
>> datetimeindex = datetimeindex.tz_localize(datetime.timezone.utc)
>> datetimeindex
DatetimeIndex(['2018-02-08 15:00:00.168456358+00:00'], dtype='datetime64[ns, UTC+00:00]', freq=None)
>> datetimeindex.__getitem__(0)
Timestamp('2018-02-08 15:00:00.168456358+0000', tz='UTC+00:00')
>> datetimeindex.__iter__().__next__()
Timestamp('2018-02-08 15:00:00.168456+0000', tz='UTC+00:00')
```
#### Problem description

When using localize DateTimeIndex with nanosecond precision, __getitem__ behavious differs from __iter__().__next__ behaviour, as when iterating thought the DateTimeIndex the date is round to microseconds. This doen not happends if the DatetimeIndex has no timezone.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.4.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-0.bpo.2-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 36.5.0
Cython: None
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
