BUG: Dtypes change when using `replace` with nullable dtypes
- [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.

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

---

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

```python
floats = pd.Series([1.0, 2.0, 3.999, 4.4], dtype=pd.Float64Dtype())
ints = pd.Series([1, 2, 3, 4], dtype=pd.Int64Dtype())

assert floats.replace({1.0: 9}).dtype == 'int64' # results in [9, 2, 3, 4]
assert floats.replace({1.0: 9.0}).dtype == 'float64'

assert ints.replace({1: 9}).dtype == 'int64'
assert ints.replace({1: 9.0}).dtype == 'float64'
```

#### Problem description

The dtype of the resulting series is being changed when `replace` is being called with `Int64Dtype` and `Float64Dtype` series'.

There are two problems here, from what I can tell:
- We lose the nullable dtypes in favor of `int64` and `float64`.
- When doing `replace` on a float series with an integer value, the resulting series gets converted to ints, truncating the decimals

#### Expected Output

```python
assert floats.replace({1.0: 9}).dtype == pd.Float64Dtype() # Matching how an int passed to replace gets converted to a float with float64
assert floats.replace({1.0: 9.0}).dtype == pd.Float64Dtype() 

assert ints.replace({1: 9}).dtype == pd.Int64Dtype()
assert ints.replace({1: 9.0}).dtype ==  pd.Float64Dtype() # Matching how int -> float if a float is passed to replace

```

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

<details>

INSTALLED VERSIONS
------------------
commit           : f2c8480af2f25efdbd803218b9d87980f416563e
python           : 3.8.2.final.0
python-bits      : 64
OS               : Darwin
OS-release       : 19.6.0
Version          : Darwin Kernel Version 19.6.0: Sun Jul  5 00:43:10 PDT 2020; root:xnu-6153.141.1~9/RELEASE_X86_64
machine          : x86_64
processor        : i386
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 1.2.3
numpy            : 1.19.5
pytz             : 2021.1
dateutil         : 2.8.1
pip              : 21.0.1
setuptools       : 41.2.0
Cython           : None
pytest           : 6.0.1
hypothesis       : None
sphinx           : 3.2.1
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.3
IPython          : 7.18.1
pandas_datareader: None
bs4              : None
bottleneck       : None
fsspec           : 0.8.7
fastparquet      : None
gcsfs            : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 3.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.6.2
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : None
xlrd             : None
xlwt             : None
numba            : None
</details>
