CLN: consider deprecating convert_floats from read_excel
As the [docs](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files) explain, the binary spreadsheet formats (xls, xlsb) store all numbers as floats, so by default pandas tries to convert floats to integers if it doesn’t lose information (1.0 --> 1). "You can pass ``convert_float=False` to disable this behavior, which may give a slight performance improvement." I tested this on four file types for a spreadsheet of ~440,000 cells, and recorded the best times out of 10 repetitions:

File type | convert_floats=True | convert_floats=False | speed up
-|-|-|-
xls |1.081|1.036 | 4.2%
xlsb | 3.413 |  3.357  |  1.6%
ods | 27.798 | 27.770  |  0.1%
xlsx | 5.182 | 5.189 |  -0.1%

`convert_floats` was probably written for the benefit of .xls files, but the benefit is minor. The .xlsx files even have a slight penalty because openpyxl already converts floats to int where possible, and so pandas converts them back to float if `convert_floats=False`.

Since .xlsx files are now the most common spreadsheet format (citation: google search), and `convert_floats` only exists for performance, is it time to remove `convert_floats`? The spreadsheet engines would keep the behaviour of `convert_floats=True` and the argument would be deprecated. This change would simplify all four engines, and if anybody really needs their ints as floats, they can always specify a `dtype`. Note: this possible deprecation came up in https://github.com/pandas-dev/pandas/issues/8212#issuecomment-54804297 before `dtype` was finalized in `read_excel`.

I can work on this if the community likes the idea.