PERF: speed up concat on Series by skipping unnecessary DataFrame creation
Removes an unnecessary `DataFrame` creation when dealing solely with `Series` objects, which reduces runtime of `concat`.

- [ ] xref #23362
- [x] tests passed
- [x] passes `git diff upstream/master -u -- "*.py" | flake8 --diff`
- [x] whatsnew entry

### Performance Comparison
```python
import pandas as pd
import numpy as np
a = np.random.randint(2000, 2100, size=100000)  # larger n than #23362 
b = np.random.randint(2000, 2100, size=100000)

x = pd.core.arrays.period_array(a, freq='B')
y = pd.core.arrays.period_array(b, freq='B')

s = pd.Series(x)
t = pd.Series(y)
```

#### Baseline
```python
%timeit x._concat_same_type([x, y])
202 µs ± 10.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

%timeit pd.concat([s, t], ignore_index=True)
839 µs ± 32.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```

#### After
```python
%timeit pd.concat([s, t], ignore_index=True)
396 µs ± 12.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
And the excised code itself:
```python
In [13]: pd.DataFrame()._get_axis_number(0)
Out[13]: 0

In [14]: %timeit pd.DataFrame()._get_axis_number(0)
Out[14]: 312 µs ± 23.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
```
So roughly 40% of the runtime was being spent mapping `axis=0 -> axis=0`