BUG: pd.to_timedelta() fails when arg is a Series with Int64 dtype
- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this bug exists on the latest version of pandas.

- [X] (optional) I have confirmed this bug exists on the master branch of pandas.

---

#### Code Sample, a copy-pastable example

```python
import pandas as pd


series = pd.Series([pd.NA], dtype="Int64")

pd.to_timedelta(series, errors="coerce")
```

#### Problem description

When converting a `Series` with an `Int64` dtype to a timedelta, `to_timedelta` fails with
 
```python
ValueError: cannot convert to 'int64'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.
```

Passing a Python list containing a NA value works, as does passing an `Int64` Series that does not contain NA values. The functionality between these cases should be equivalent.


#### Expected Output

`pd.to_timedelta()` output returns a `timedelta64` Series containing NA values when the input is an `Int64` Series containg NA values:

```python
0   <NA>
dtype: timedelta64[ns]
```

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

<details>

INSTALLED VERSIONS
------------------
commit           : 3b6915ef9435b13ac5d8c19634270ae231f8e47c
python           : 3.7.7.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.4.0-7634-generic
Version          : #38~1595345317~20.04~a8480ad-Ubuntu SMP Wed Jul 22 15:13:45 UTC 
machine          : x86_64
processor        : 
byteorder        : little
LC_ALL           : C.UTF-8
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.0.dev0+28.g3b6915ef9
numpy            : 1.18.5
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.1
setuptools       : 45.2.0.post20200210
Cython           : 0.29.21
pytest           : 6.0.1
hypothesis       : 5.23.11
sphinx           : 3.1.1
blosc            : None
feather          : None
xlsxwriter       : 1.3.1
lxml.etree       : 4.4.1
html5lib         : 1.1
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.17.0
pandas_datareader: None
bs4              : 4.9.1
bottleneck       : 1.3.2
fsspec           : 0.8.0
fastparquet      : 0.4.1
gcsfs            : 0.6.2
matplotlib       : 3.2.1
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.4
pandas_gbq       : None
pyarrow          : 0.16.0
pytables         : None
pyxlsb           : None
s3fs             : 0.4.2
scipy            : 1.5.2
sqlalchemy       : 1.3.18
tables           : 3.6.1
tabulate         : 0.8.7
xarray           : 0.16.0
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.50.1

</details>
