Constructing a DatetimeArray with pd.array?
I wanted to create a naive `DatetimeArray` with `pd.array`, but that doesn't seem very easy or even impossible:

Specyfing `dtype='datetime64[ns]'` gives a numpy backed PandasArray:
```
In [7]: pd.array(['2012-01-01', '2012-01-02'], dtype='datetime64[ns]')
Out[7]: 
<PandasArray>
[numpy.datetime64('2012-01-01T00:00:00.000000000'), numpy.datetime64('2012-01-02T00:00:00.000000000')]
Length: 2, dtype: datetime64[ns]
```

We also can't specify a custom dtype, as it needs a tz:
```
In [8]: pd.array(['2012-01-01', '2012-01-02'], dtype=pd.DatetimeTZDtype(tz=None))  
...
TypeError: A 'tz' is required.
```

Passing a DatetimeIndex also gives a PandasArray:
```
In [9]: pd.array(pd.date_range('2012-01-01', periods=3)) 
Out[9]: 
<PandasArray>
[numpy.datetime64('2012-01-01T00:00:00.000000000'),
 numpy.datetime64('2012-01-02T00:00:00.000000000'),
 numpy.datetime64('2012-01-03T00:00:00.000000000')]
Length: 3, dtype: datetime64[ns]
```

Should one of those give a DatetimeArray instead of PandasArray?

