Decoding issue when reading in py3 a datetime64 hdf data that was created in py2
#### Code Sample, a copy-pastable example if possible

### In Python 2, create the `.hdf` file with a `datetime64[ns]` column

```python
#python 2
>>> d = {"data": pd.Timestamp("2020-02-06T18:00")}
>>> df = pd.DataFrame(d, index=[1])
>>> df
                 data
1 2020-02-06 18:00:00
>>> df.dtypes
data    datetime64[ns]
dtype: object
>>> df.to_hdf('py2_data.hdf', 'py2_data')
```
### Reading the hdf in python 2 works just fine

```python
# python2
>>> df = pd.read_hdf('py2_data.hdf', 'py2_data')
>>> df
                 data
1 2020-02-06 18:00:00
>>> df.dtypes
data    datetime64[ns]
dtype: object
```

## Reading the hdf in **Python 3** the dtype of the data column is set to `int64` instead of `datatime64`

```python
# python 3
>>> df = pd.read_hdf('py2_data.hdf', 'py2_data')
>>> df
                  data
1  1581012000000000000
>>> df.dtypes
data    int64
dtype: object
```
#### Problem description

The reason why the _dtype_ is being loaded as a `int64` in python 3 is because pandas is not reading correctly the `value_type` attribute from the hdf file. The current implementation does *not* explicitly decodes to "UTF-8" the attribute value when its type is `np.bytes_`, which causes it to read the dtype as `b'datetime64` instead of the correct `datetime64` value. That, in turn, causes it to treat the raw value `1581012000000000000` with an inferred dtype of `int64` instead of coercing it to a `M8[ns]` dtype that would give the correct `2020-02-06 18:00:00` datetime value.

This is a similar problem to the one from issue #15725, but that one was when reading the _index_ from the _hdf_ but this one is when reading the data.

The fix for this seems to be really straight forward, you just need to ensure the value of the`value_type` attribute is decoded to _UTF-8_ when reading it from the hdf file. More specifically, [in this line of pytables.py](https://github.com/pandas-dev/pandas/blob/v1.0.1/pandas/io/pytables.py#L2727)

```diff
diff --git a/pytables.py b/pytables.py
index 06c9aa1..9d074d5 100644
--- a/pytables.py
+++ b/pytables.py
@@ -2724,7 +2724,7 @@ class GenericFixed(Fixed):
         if isinstance(node, tables.VLArray):
             ret = node[0][start:stop]
         else:
-            dtype = getattr(attrs, "value_type", None)
+            dtype = _ensure_decoded(getattr(attrs, "value_type", None))
             shape = getattr(attrs, "shape", None)
```

I can make the change and open a PR, as it seems to be really straight forward, just let me know what you prefer.

Note: this is an issue only when the ".hdf" file is created in python 2 using the _fixed_ format. If it is created using the _table_ format it works as expected.

Thanks!

#### Expected Output
The expected output is that in python 3 it will parse the dtype of the columns correctly, so in this case it should be:

```python
>>> df = pd.read_hdf('py2_data.hdf', 'py2_data')
>>> df
                 data
1 2020-02-06 18:00:00
>>> df.dtypes
data    datetime64[ns]
dtype: object
```

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

<details>
```
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.10.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 18.7.0
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 20.0.2
setuptools       : 45.1.0.post20200127
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : 3.6.1
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
```
</details>
