Right now, IntegerArary.__add__ calls np.asarray(other) for list-like other. When other is an actual list with missing values, we end up with a float ndarray.

In [2]: arr = pd.Series([1, None, 3], dtype='Int8').values

In [3]: arr + list(arr)
Out[3]: array([ 2., nan,  6.])

Compare with Non-NA values:

In [4]: arr = pd.Series([1, 2, 3], dtype='Int8').values

In [6]: arr + list(arr)
Out[6]: IntegerArray([2, 4, 6], dtype='Int8')

In general, an IntegerArray + ndarray[float] should be a floating point ndarray,

In [10]: arr + np.array([1., 2., 3.])
Out[10]: array([2., 4., 6.])

Do we special case missing values here?