API: Should extended dtype work as the same as np.dtype?
We can create / convert using datetimetz dtype, but it doesn't work some cases.

```
s = pd.Series([pd.Timestamp('2011-01-31', tz='US/Eastern')])
s
#0   2011-01-31 00:00:00-05:00
# dtype: datetime64[ns, US/Eastern]

# OK
s.astype('datetime64[ns, Asia/Tokyo]')
#0   2011-01-31 14:00:00+09:00
# dtype: datetime64[ns, Asia/Tokyo]
```
### astype

```
# numpy (OK)
pd.Series([1296432000000000000]).astype('datetime64[ns]')
#0   2011-01-31
# dtype: datetime64[ns]

# extended (NG)
pd.Series([1296432000000000000]).astype('datetime64[ns, Asia/Tokyo]')
# TypeError: Invalid datetime unit in metadata string "[ns, Asia/Tokyo]"
```
### dtype arg

```
# extended (OK ? I think the result should be 2011-01-31 00:00:00-05:00... see below)
pd.Series([1296432000000000000], dtype='datetime64[ns, US/Eastern]')
#0   2011-01-31 05:00:00-05:00
# dtype: datetime64[ns, US/Eastern]

# ref
pd.Series([1296432000000000000], dtype='datetime64[ns]').dt.tz_localize('US/Eastern')
#0   2011-01-31 00:00:00-05:00
# dtype: datetime64[ns, US/Eastern]
```

```
# extended (NG)
pd.Series([pd.Timestamp('2011-01-01', tz='US/Eastern')], dtype='datetime64[ns, US/Eastern]')
# TypeError: data type not understood
```
