PERF: DataFrame constructor from list dataclasses 
### 

- [X] I have checked that this issue has not already been reported.

- [X] I have confirmed this issue exists on the [latest version](https://pandas.pydata.org/docs/whatsnew/index.html) of pandas.

- [X] I have confirmed this issue exists on the master branch of pandas.


### Reproducible Example

```
@dataclass()
class Example:
    first: int
    second: int

class_list = [Example(random.randint(0,1000), random.randint(0,1000)) for x in range(1000)]
```
`pd.DataFrame(class_list)`
6.1 ms ± 902 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

This is probably because the constructor uses asdict which is quite slow, think we can make the constructor work without asdict, something along these lines:

`pd.DataFrame([(x.first, x.second) for x in  class_list], columns = ['first', 'second'])`
653 µs ± 58.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

### Installed Versions

<details>

INSTALLED VERSIONS
------------------
commit           : 945c9ed766a61c7d2c0a7cbb251b6edebf9cb7d5
python           : 3.8.5.final.0
python-bits      : 64
OS               : Linux
OS-release       : 5.11.0-38-generic
Version          : #42~20.04.1-Ubuntu SMP Tue Sep 28 20:41:07 UTC 2021
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_IL
LOCALE           : en_IL.UTF-8

pandas           : 1.3.4
numpy            : 1.19.2
pytz             : 2020.1
dateutil         : 2.8.1
pip              : 20.2.4
setuptools       : 50.3.1.post20201107
Cython           : 0.29.21
pytest           : 6.1.1
hypothesis       : None
sphinx           : 3.2.1
blosc            : None
feather          : None
xlsxwriter       : 1.3.7
lxml.etree       : 4.6.1
html5lib         : 1.1
pymysql          : None
psycopg2         : 2.9.1 (dt dec pq3 ext lo64)
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : 4.9.3
bottleneck       : 1.3.2
fsspec           : 0.8.3
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.2
numexpr          : 2.7.1
odfpy            : None
openpyxl         : 3.0.5
pandas_gbq       : 0.14.1
pyarrow          : 5.0.0
pyxlsb           : None
s3fs             : None
scipy            : 1.5.2
sqlalchemy       : 1.3.20
tables           : 3.6.1
tabulate         : 0.8.9
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
numba            : 0.51.2
</details>


### Prior Performance

_No response_