CLN: use idiomatic pandas_dtypes in pandas/dtypes/common.py
closes #24593 

Some benchmarks of ``pandas_dtype`` construction from a dtype object & strings, only thing slightly suprising is ``Period[D]``
```
[ 87.50%] ··· ============================================ =============
                                 dtype                                  
              -------------------------------------------- -------------
                             dtype('int64')                   463±20ns  
                             dtype('int32')                   450±20ns  
                            dtype('uint32')                   444±4ns   
                            dtype('uint64')                   484±20ns  
                            dtype('float32')                  494±30ns  
                            dtype('float64')                  507±30ns  
                             dtype('int16')                   471±30ns  
                             dtype('int8')                    506±30ns  
                            dtype('uint16')                   505±40ns  
                             dtype('uint8')                   485±20ns  
                              dtype('<M8')                    451±20ns  
                              dtype('<m8')                    480±20ns  
                               dtype('O')                    627±200ns  
                  pandas.core.arrays.integer.Int8Dtype        995±60ns  
                 pandas.core.arrays.integer.Int16Dtype        944±60ns  
                 pandas.core.arrays.integer.Int32Dtype        939±70ns  
                 pandas.core.arrays.integer.Int64Dtype        988±30ns  
                 pandas.core.arrays.integer.UInt8Dtype        924±50ns  
                 pandas.core.arrays.integer.UInt16Dtype       954±60ns  
                 pandas.core.arrays.integer.UInt32Dtype       966±70ns  
                 pandas.core.arrays.integer.UInt64Dtype     1.00±0.06μs 
               pandas.core.dtypes.dtypes.CategoricalDtype     978±30ns  
                pandas.core.dtypes.dtypes.IntervalDtype      929±300ns  
                          datetime64[ns, UTC]               1.52±0.03μs 
                               period[D]                      958±9ns   
                                 int64                       16.1±0.1μs 
                                 int32                       16.1±0.6μs 
                                 uint32                      16.0±0.1μs 
                                 uint64                      16.2±0.3μs 
                                float32                      15.8±0.4μs 
                                float64                      15.7±0.6μs 
                                 int16                       16.0±0.2μs 
                                  int8                      15.7±0.06μs 
                                 uint16                     15.9±0.08μs 
                                 uint8                       17.3±0.6μs 
                               datetime64                    16.2±0.3μs 
                              timedelta64                    16.0±0.1μs 
                                 object                      16.0±0.2μs 
                                  Int8                       6.24±0.1μs 
                                 Int16                       7.41±0.2μs 
                                 Int32                      8.15±0.04μs 
                                 Int64                       9.39±0.1μs 
                                 UInt8                       10.1±0.1μs 
                                 UInt16                     11.0±0.04μs 
                                 UInt32                      11.8±0.5μs 
                                 UInt64                     12.7±0.08μs 
                                category                    2.62±0.03μs 
                                interval                     6.72±0.2μs 
                          datetime64[ns, UTC]                2.93±0.1μs 
                               period[D]                     52.6±0.6μs 
              ============================================ =============
```