[lang] Free ndarray memory on the base class

[BernardoCovas]

classes inheriting from Ndarray should have their memory released

Issue: #8763

Brief Summary

ScalarNdarray implements del() to allow python to get rid of the allocated memory,
but other subclasses of Ndarray (like VectorNdarray() or MatrixNdarray()) don't implement the same behaviour.
Seems to me the del() method should be moved to the Ndarray class.
I tested this simple change and indeed it fixes the memory leak.

Walkthrough

import taichi as ti
import taichi.types as tt

ti.init(ti.gpu)

while True:
    arr = ti.ndarray(tt.vector(3, ti.f32), (1000, 1000))
    arr = arr.to_numpy()
    # whatch gpu memory skyrocket

If, instead:

import taichi as ti
import taichi.types as tt

ti.init(ti.gpu)

while True:
    arr = ti.ndarray(ti.f32, (1000, 1000, 3))
    arr = arr.to_numpy()
    # whatch gpu memory basically stay the same

---

Note

Move deletion logic to Ndarray.__del__ so all ndarray variants free device memory; remove subclass-specific __del__ from ScalarNdarray.

• Memory management:
  • Add Ndarray.__del__ to delete underlying arr via rt.prog.delete_ndarray(...) when runtime/program exist.
  • Remove ScalarNdarray.__del__; destruction is now handled centrally by base class for all ndarray subclasses.
• Files:
  • Updated python/taichi/lang/_ndarray.py to centralize deletion logic and add safety checks (impl/arr/runtime/prog presence).

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