unique(), values discards timezone information in some cases
Not sure if it's an expected behavior. This seems strange to me.

#### A small, complete example of the issue

```python
rng = pd.date_range('1/1/2011', periods=5, freq='D', tz='utc')
rng  # Timezone is good
```
>>>    DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
                   '2011-01-05'],
                  dtype='datetime64[ns, UTC]', freq='D')

```python
rng.unique()  # Timezone is still good
```
>>>    DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03', '2011-01-04',
                   '2011-01-05'],
                  dtype='datetime64[ns, UTC]', freq='D')

```python
rng.values  # Timezone lost!
```
>>>    array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
       '2011-01-03T00:00:00.000000000', '2011-01-04T00:00:00.000000000',
       '2011-01-05T00:00:00.000000000'], dtype='datetime64[ns]')

```python
serie = rng.to_series().reset_index()['index']
serie  # Timezone is good
```
>>> 0   2011-01-01 00:00:00+00:00
1   2011-01-02 00:00:00+00:00
2   2011-01-03 00:00:00+00:00
3   2011-01-04 00:00:00+00:00
4   2011-01-05 00:00:00+00:00
Name: index, dtype: datetime64[ns, UTC]

```python
serie.unique()  # Timezone is lost!
```
>>>    array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
           '2011-01-03T00:00:00.000000000', '2011-01-04T00:00:00.000000000',
           '2011-01-05T00:00:00.000000000'], dtype='datetime64[ns]')

```python
serie.values  # Timezone is lost!
```
>>>    array(['2011-01-01T00:00:00.000000000', '2011-01-02T00:00:00.000000000',
           '2011-01-03T00:00:00.000000000', '2011-01-04T00:00:00.000000000',
           '2011-01-05T00:00:00.000000000'], dtype='datetime64[ns]')

#### Expected Output
Outputs of pd.DatetimeIndex.values, pd.Series.unique() and pd.Series.values should be preserve the timezone information.

Note: I did a quick test with Python 3.4.5 and pandas 0.19
only difference is (to be clear, rng.values and serie.values still discard the tzinfo):
```python
serie.unique()  # Timezone is good again!! but dtype is object (preserving pd.Timestamp)
```
>>>    array([Timestamp('2011-01-01 00:00:00+0000', tz='UTC', freq='D'),
       Timestamp('2011-01-02 00:00:00+0000', tz='UTC', freq='D'),
       Timestamp('2011-01-03 00:00:00+0000', tz='UTC', freq='D'),
       Timestamp('2011-01-04 00:00:00+0000', tz='UTC', freq='D'),
       Timestamp('2011-01-05 00:00:00+0000', tz='UTC', freq='D')], dtype=object)



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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.4.4.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.18.1
nose: None
pip: 8.1.2
setuptools: 23.0.0
Cython: None
numpy: 1.11.0
scipy: 0.17.1
statsmodels: 0.6.1
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: 1.0.0
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: None
xlsxwriter: 0.9.2
lxml: 3.6.0
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: 0.7.6.None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1
</details>
