Merge pyodide_py into python_stdlib.zip. ### Description

Merges pyodide_py.tar into python_stdlib.zip. This reduces the number of network requests during the initialization.

The original intention of splitting pyodide_py.tar from pyodide.asm.data was to give users an option to provide custom files (#2023), which is now available by modifying python_stdlib.zip directly. So I think there is no reason to have both python_stdlib.zip and pyodide_py.tar.

### Checklists

- [x] Add a [CHANGELOG](https://github.com/pyodide/pyodide/blob/main/docs/project/changelog.md) entry
- [x] Add / update tests

# setup: import numpy ; a = numpy.array([ [i/10., i/10., i/20.] for i in range(44440)], dtype=numpy.double)
# run: hyantes(0, 0, 90, 90, 1, 100, 80, 80, a)

# pythran export hyantes(float, float, float, float, float, float, int,
# int, float[][])
import numpy as np


def hyantes(xmin, ymin, xmax, ymax, step, range_, range_x, range_y, t):
    X, Y = t.shape
    pt = np.zeros((X, Y))
    for i in range(X):
        for j in range(Y):
            for k in t:
                tmp = 6368.0 * np.arccos(
                    np.cos(xmin + step * i)
                    * np.cos(k[0])
                    * np.cos((ymin + step * j) - k[1])
                    + np.sin(xmin + step * i) * np.sin(k[0])
                )
                if tmp < range_:
                    pt[i, j] += k[2] / (1 + tmp)
    return pt

# setup: import numpy ; a = numpy.array([ [i/10., i/10., i/20.] for i in range(44440)], dtype=numpy.double)
# run: hyantes(0, 0, 90, 90, 1, 100, 80, 80, a)

# pythran export hyantes(float, float, float, float, float, float, int,
# int, float[][])
import numpy as np


def hyantes(xmin, ymin, xmax, ymax, step, range_, range_x, range_y, t):
    X, Y = t.shape
    pt = np.zeros((X, Y))
    for i in range(X):
        for j in range(Y):
            for k in t:
                tmp = 6368.0 * np.arccos(
                    np.cos(xmin + step * i)
                    * np.cos(k[0])
                    * np.cos((ymin + step * j) - k[1])
                    + np.sin(xmin + step * i) * np.sin(k[0])
                )
                if tmp < range_:
                    pt[i, j] += k[2] / (1 + tmp)
    return pt
