pd.concat of Series with int64 column and Series with int64-ExtensionArray yields int64
#### Code Sample

```python
import fletcher as fr
import pandas as pd

df_ext = pd.DataFrame({'a': fr.FletcherArray([1, 2])})
df_ext.info()
# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 2 entries, 0 to 1
# Data columns (total 1 columns):
# a    2 non-null fletcher[int64]
# dtypes: fletcher[int64](1)
# memory usage: 100.0 bytes

df_normal = pd.DataFrame({'a': [3, 4]})
df_normal.info()
# <class 'pandas.core.frame.DataFrame'>
# RangeIndex: 2 entries, 0 to 1
# Data columns (total 1 columns):
# a    2 non-null int64
# dtypes: int64(1)
# memory usage: 96.0 bytes

# Works
pd.concat([df_ext, df_normal]).info()
# <class 'pandas.core.frame.DataFrame'>
# Int64Index: 4 entries, 0 to 1
# Data columns (total 1 columns):
# a    4 non-null object
# dtypes: object(1)
# memory usage: 64.0+ bytes

# yield int64 instead of object
pd.concat([df_ext['a'], df_normal['a']]).dtype
# dtype('int64')
```

#### Problem description

This currently leads `BaseReshapingTests.test_concat_mixed_dtypes` to fail on ExtensionArrays that can be converted to any numeric data NumPy datatype. 