ASV: add frame ops benchmarks for varying n_rows/n_columns ratios
Currently, we have a benchmark for frame/frame ops with a wide dataframe (originating from previous (long) PR / discussion about this, xref https://github.com/pandas-dev/pandas/pull/32779)

To get a better idea of the trade-offs for future changes to the ops code (eg ArrayManager related), I think it is useful to have this benchmark for a varying rows/columns ratio (it is mostly this ratio that determines how much overhead we have when performing ops column-wise). 

So I changed the benchmark to be parametrized over 4 different ratios, from a long to a wide dataframe, and with each case having the same number of total elements in the dataframe. 

The shape in the original benchmark was `(500, 2000)`, which is somewhere in between two new cases of  `(10_000, 1000)` and `(1000, 10_000)`, so that should preserve the original intent of the benchmark (the last case of `(1000, 10_000)` even has a lower row/column ratio, so should even be a "worse" case for the column-wise ops perspective).

cc @jbrockmendel 
A run of those benchmarks:

```
$ asv dev -b FrameWithFrameWide
· Discovering benchmarks
· Running 2 total benchmarks (1 commits * 1 environments * 2 benchmarks)
[  0.00%] ·· Benchmarking existing-py_home_joris_miniconda3_envs_dev_bin_python
[ 25.00%] ··· arithmetic.FrameWithFrameWide.time_op_different_blocks          
[ 25.00%] ··· ============================== =============== =============== =============== ===============
              --                                                          shape                             
              ------------------------------ ---------------------------------------------------------------
                            op                (1000000, 10)   (100000, 100)   (10000, 1000)   (1000, 10000) 
              ============================== =============== =============== =============== ===============
                 <built-in function add>         19.1±0ms        14.9±0ms        14.1±0ms        14.3±0ms   
               <built-in function floordiv>      435±0ms         475±0ms         418±0ms         375±0ms    
                  <built-in function gt>         12.1±0ms        11.8±0ms        12.6±0ms        11.8±0ms   
              ============================== =============== =============== =============== ===============

[ 50.00%] ··· arithmetic.FrameWithFrameWide.time_op_same_blocks               
[ 50.00%] ··· ============================== =============== =============== =============== ===============
              --                                                          shape                             
              ------------------------------ ---------------------------------------------------------------
                            op                (1000000, 10)   (100000, 100)   (10000, 1000)   (1000, 10000) 
              ============================== =============== =============== =============== ===============
                 <built-in function add>         10.6±0ms        11.7±0ms        10.5±0ms        12.3±0ms   
               <built-in function floordiv>      233±0ms         229±0ms         258±0ms         230±0ms    
                  <built-in function gt>         5.64±0ms        6.29±0ms        5.51±0ms        7.50±0ms   
              ============================== =============== =============== =============== ===============
```

So the floordiv one is quite slow. I could maybe reduce the number of rows with a factor of 10 in that case. > So the floordiv one is quite slow. I could maybe reduce the number of rows with a factor of 10 in that case.
sure nitpick, otherwise LGTM