"these third party libraries are using, like reliance on iterator protocol, descriptor protocol, metaclasses, context managers, Python data model special methods, dynamic attribute lookups, etc.,"

Julia can Jit code that relies on vastly improved versions of all those features (minus some setatr etc).

Can you use them on a custom array type in any case let alone scipy? No. and that's the point. In julia, the optim package for example takes in abstract arrays with all those features. Mathematical assumptions are categorically different than unavailable or slow PL semantics.

Julia preserves full general programming language semantics including zero cost abstractions, higher order functions, closures, zero cost differentiation and extending custom types, while still being fast enough for numerical computing (minus exceptions).

It seems to be that Python is sticking with 1d figures and that's assuming your monkey patch idea works, also ignoring the ecosystem cost and the fact that monkeypatching in Julia is part of normal code design (through multimethods) whereas in python it's a code smell.

I think you are really grasping at straws here.