BUG: HDFStore doesn't save datetime64[s] right about pandas HOT 1 CLOSED

**** This doesn't work
Calling _read_group()
type of Storer:  <class 'pandas.io.pytables.FrameFixed'>
Calling BlockManagerFixed.read()
Calling GenericFixed.read_array()
Checking dtype in read_array(): datetime64[s]
Checking value read from node: [[ 978307200]
 [1012608000]]    # It is correct so far.
Checking value after reconstructing time zone: <DatetimeArray>
[
['1970-01-01 00:00:00.978307200'],
['1970-01-01 00:00:01.012608']
]
Shape: (2, 1), dtype: datetime64[ns]   # So the time zone attribution messed it up somehow.
block0_values <DatetimeArray>
[
['1970-01-01 00:00:00.978307200', '1970-01-01 00:00:01.012608']
]
Shape: (1, 2), dtype: datetime64[ns]
columns Index([0], dtype='int64')
###### result:                                0
0 1970-01-01 00:00:00.978307200
1 1970-01-01 00:00:01.012608000


**** However this does work
Calling _read_group()
type of Storer:  <class 'pandas.io.pytables.FrameFixed'>
Calling BlockManagerFixed.read()
Calling GenericFixed.read_array()
Checking dtype in read_array(): datetime64[ns]
Checking value read from node: [[ 978307200000000000]
 [1012608000000000000]]
Checking value after reconstructing time zone: <DatetimeArray>
[
['2001-01-01 00:00:00'],
['2002-02-02 00:00:00']
]
Shape: (2, 1), dtype: datetime64[ns]
block0_values <DatetimeArray>
[
['2001-01-01 00:00:00', '2002-02-02 00:00:00']
]
Shape: (1, 2), dtype: datetime64[ns]
columns Index([0], dtype='int64')
###### result:             0
0 2001-01-01
1 2002-02-02