Bug: read_csv losing precision when reading Int64 data with N/A values
#### Code Sample

```
import pandas as pd
import numpy as np
from io import StringIO
TESTDATA1 = StringIO("""col1;col2
     1;123
     2;456
     3;1582218195625938945
     """)
TESTDATA2 = StringIO("""col1;col2
     1;123
     2;
     3;1582218195625938945
     """)
D1 = pd.read_csv(TESTDATA1, sep=";", dtype={'col2':'Int64'})
D2 = pd.read_csv(TESTDATA2, sep=";", dtype={'col2':'Int64'})
D1['col2']
D2['col2']
D1.loc[2,'col2'] == D2.loc[2,'col2']
```

Yields : 
```
>>> D1['col2']
0                    123
1                    456
2    1582218195625938945
Name: col2, dtype: Int64
>>> D2['col2']
0                    123
1                   <NA>
2    1582218195625938944
Name: col2, dtype: Int64
>>> D1.loc[2,'col2'] == D2.loc[2,'col2']
False
```

#### Problem description

When pd.read_csv reads a column with Nullable Int64 data, and the column contains missing values (N/A), the precision on other values in that column is not maintained.


#### Expected Output
```
>>> D1['col2']
0                    123
1                    456
2    1582218195625938945
Name: col2, dtype: Int64
>>> D2['col2']
0                    123
1                   <NA>
2    1582218195625938945
Name: col2, dtype: Int64
>>> D1.loc[2,'col2'] == D2.loc[2,'col2']
True
```

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

<details>
>>> pd.show_versions()

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.3.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.18.0-3-amd64
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : en_US.UTF-8

pandas           : 1.0.1
numpy            : 1.18.1
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 18.1
setuptools       : 40.8.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 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           : None
pyxlsb           : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : None
</details>
