Using to_json/read_json with orient='table' on a DataFrame with a single level MultiIndex does not work
#### Code Sample, a copy-pastable example if possible

```python
import pandas

#Scenario1
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,),(2,),(3,),(4,)]))
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
new_df=pandas.read_json(js, orient='table') # this runs but creates NaN indexes

#Scenario2
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,),(2,),(3,),(4,)], names=['ind1']))
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
try:
    new_df==pandas.read_json(js, orient='table')  # this fails with KeyError: 'None of [None] are in the columns'
except KeyError as e:
    pass

#Scenario3
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,1),(1,2),(2,1),(2,2)], names=['ind1', 'ind2']))
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
new_df=pandas.read_json(js, orient='table')  # this works as is is multi level multiindex

#Scenario4
df=pandas.DataFrame(data=[[1,1],[2,2],[3,3],[4,4]],columns=['A','B'], index=pandas.MultiIndex.from_tuples([(1,),(2,),(3,),(4,)]))
df.index=df.index.to_flat_index() # a workaround?
js=df.to_json()
pandas.read_json(js) #ok
js=df.to_json(orient='table')
new_df=pandas.read_json(js, orient='table') # This is a workaround that does produce sensible results

```
#### Problem description

I have a DataFrame that has a MultiIndex, even though that index only has one level and could easily be a normal index, I am receiving this table from ipyaggrid, so am not in control of that.

Saving this table to json with df.to_json(orient='table) and then loading from pandas.read_json(js, orient='table) leads to either an exception or the index being changed to NaN values. 

Neither of these outcomes seems desirable or correct. 

I have a workaround to intercept DataFrames with this feature and convert their index so a flat Index, but it would be good if the table serialisation just worked. 

I was drawn to orient='Table' by the comments on this similar issue, my main requirement here is to save/load the DataFrame preserving its row and column order. You don't get this with the standard to_json/from_json modes
https://github.com/pandas-dev/pandas/issues/4889 

#### Expected Output
new_df for scenario1 and scenario2 to should have a MultiIndex, in the first scenario with a level that has no name and in the second scenario with level called 'ind1'

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

<details>

pandas.show_versions()
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.7.final.0
python-bits      : 64
OS               : Windows
OS-release       : 7
machine          : AMD64
processor        : Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 0.25.0
numpy            : 1.17.0
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 10.0.1
setuptools       : 39.1.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.9.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None
</details>
