REGR: AssertionError when subtracting Timestamp-valued DataFrames with non-indentical column index
```python
import pandas as pd

df = pd.DataFrame(
    {
        "foo": [pd.Timestamp("2019"), pd.Timestamp("2020")],
        "bar": [pd.Timestamp("2018"), pd.Timestamp("2021")],
    }
)

df2 = df[["foo"]]

print(df - df2)
```
#### Problem description

The above snippet raises the following exception:

```
Traceback (most recent call last):
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 149, in na_arithmetic_op
    result = expressions.evaluate(op, str_rep, left, right)
  File ".v
env/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 208, in evaluate
    return _evaluate(op, op_str, a, b)
  File ".venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 70, in _evaluate_standard
    return op(a, b)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/common.py", line 64, in new_method
    return method(self, other)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 500, in wrapper
    result = arithmetic_op(lvalues, rvalues, op, str_rep)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 192, in arithmetic_op
    res_values = dispatch_to_extension_op(op, lvalues, rvalues)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/dispatch.py", line 125, in dispatch_to_extension_op
    res_values = op(left, right)
  File ".venv/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 1390, in __rsub__
    f"cannot subtract {type(self).__name__} from {type(other).__name__}"
TypeError: cannot subtract DatetimeArray from ndarray

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "pandas_bug.py", line 36, in <module>
    print(df2 - df)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 703, in f
    new_data = left._combine_frame(right, pass_op, fill_value)
  File ".venv/lib/python3.6/site-packages/pandas/core/frame.py", line 5297, in _combine_frame
    new_data = ops.dispatch_to_series(self, other, _arith_op)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 416, in dispatch_to_series
    new_data = expressions.evaluate(column_op, str_rep, left, right)
  File ".venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 208, in evaluate
    return _evaluate(op, op_str, a, b)
  File ".venv/lib/python3.6/site-packages/pandas/core/computation/expressions.py", line 70, in _evaluate_standard
    return op(a, b)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 385, in column_op
    return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/__init__.py", line 385, in <dictcomp>
    return {i: func(a.iloc[:, i], b.iloc[:, i]) for i in range(len(a.columns))}
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 121, in na_op
    return na_arithmetic_op(x, y, op, str_rep)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 151, in na_arithmetic_op
    result = masked_arith_op(left, right, op)
  File ".venv/lib/python3.6/site-packages/pandas/core/ops/array_ops.py", line 75, in masked_arith_op
    assert isinstance(x, np.ndarray), type(x)
```
This is a 1.0.0 regression; in 0.25.3, the operation succeeds and the unmatched `bar` column is filled with `NaN` in the output.

The same error occurs with:
* Any combination of incompatible columns (strict subset, strict superset, overlapping, disjoint)
* Calling the `subtract` method instead of using the subtraction operator
* Timezone-aware `Timestamp`s as well as timezone-naive

It does *not* seem to occur with:
* Mismatches on the row index; transposing the dataframes in the above example prevents the errors occuring.
* `pd.Series` objects with mismatched indexes (e.g. calling the above on the first row of each dataframe works fine)
* Other dtypes; `bool`, `float`, and `int` seem to work fine. Similarly, if the dataframes are explicitly cast to dtype `object`, the operation succeeds.

#### Expected Output

```
   bar    foo
0  NaN 0 days
1  NaN 0 days
```

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

<details>
```
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.8.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-74-generic
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.0.0
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.3.1
setuptools       : 41.6.0
Cython           : None
pytest           : 5.3.5
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          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
pytest           : 5.3.5
pyxlsb           : None
s3fs             : None
scipy            : 1.4.1
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
```

</details>
