gh-91407: Merge BINARY_SUBSCR_LIST_INT with BINARY_SUBSCR_TUPLE_INT

[eendebakpt]

Merge BINARY_SUBSCR_LIST_INT and BINARY_SUBSCR_TUPLE_INT. This reduces the number of opcodes by 1 and eliminates some duplicated code.

Details

The reason for this PR is not performance, but here microbenchmarks to check there is no slowdown:

list: Mean +- std dev: [base] 14.7 ns +- 0.2 ns -> [patch] 14.7 ns +- 0.2 ns: 1.00x faster
tuple: Mean +- std dev: [base] 14.6 ns +- 0.2 ns -> [patch] 14.8 ns +- 0.4 ns: 1.02x slower
mixed: Mean +- std dev: [base] 25.7 us +- 0.9 us -> [patch] 25.0 us +- 0.3 us: 1.03x faster

Geometric mean: 1.01x faster

Code

import pyperf
runner = pyperf.Runner()

setup='l=[1,2,None,4,5,6,7,8,9,10]; t=(1,None,3,4)'

runner.timeit(name=f"list", stmt=f"x=l[3]",setup=setup)
runner.timeit(name=f"tuple", stmt=f"x=t[2]",setup=setup)
code="""
for ii in range(500):
	idx=ii%4
	a=l[idx]
	b=t[idx]	
"""
runner.timeit(name=f"mixed", stmt=code,setup=setup)

See #91407

https://bugs.python.org/issue47251

[corona10]

IMHO, you may need to run all pyperformance benchmarks with a reliable machine.

[markshannon]

Please read https://github.com/python/cpython/blob/main/Python/adaptive.md#implementation-of-specialized-instructions
This adds branches into specialized instructions, which we are trying to avoid.

We could reduce code duplication by merging the code paths after the type check and array pointer extraction.
Something like:

PyObject **array

tuple:
     DEOPT_IF(Py_TYPE(obj) != &PyTuple_Type);
     array = &PyTuple_GET_ITEM(0);
     goto shared_code;
tuple:
     DEOPT_IF(Py_TYPE(obj) != &PyList_Type);
     array = &PyList_GET_ITEM(0);
     goto shared_code;