Improved scaling of ufuncs on free-threading

Improved scaling of ufuncs on free-threading

NumPy's ufuncs now scale significantly better on free-threading builds of CPython due to the following optimizations:

• Lock-free dispatch table: The ufuncs dispatch table is now implemented as a lock-free concurrent hash map, allowing multiple threads to call ufuncs without contention.

• Immortal shared objects: Certain shared objects, such as global memory handlers, have been made immortal. This effectively reduces reference counting contention across threads.

• Optimized memory allocation: NumPy now utilizes PyMem_RawMalloc and PyMem_RawFree for memory allocation. On Python 3.15 and newer, this leverages mimalloc and significantly reduces memory allocation overhead in multi-threaded workloads.