Reductions return NumPy objects eagerly but blosc2 arrays lazily

Start by tracing the reduction entry points shown in the examples: blosc2.sum, array .sum(), array .mean(), array .std(), blosc2.any(), and lazyexpr(...).compute(). Compare their return types with the Array API reduction requirements, then resolve the full-reduction, partial-reduction, and deprecation questions before defining what consistent behavior and tests would constitute done.

Reductions return NumPy objects through the eager path but blosc2 arrays through
the lazy one, so the same reduction has two different return types depending on
how it is spelled.

import numpy as np, blosc2

na = np.arange(100, dtype="f8").reshape(10, 10)
a, b = blosc2.asarray(na), blosc2.asarray(na * 2)

a.sum()                                    # numpy.float64
blosc2.sum(a)                              # numpy.float64
a.sum(axis=0)                              # numpy.ndarray
(a + b).sum(axis=0)                        # numpy.ndarray
a.mean(), a.std()                          # numpy.float64
blosc2.any(a > 5)                          # numpy.bool

blosc2.lazyexpr("sum(a + b)", {"a": a, "b": b}).compute()          # blosc2.NDArray, shape ()
blosc2.lazyexpr("sum(a + b, axis=0)", {"a": a, "b": b}).compute()  # blosc2.NDArray, shape (10,)

(a + b).compute()                          # blosc2.NDArray  (no reduction: stays blosc2)

The last group is the point: sum(a + b) written as a string expression gives a
blosc2.NDArray, while the same reduction written as (a + b).sum() gives a
numpy.float64. A caller cannot tell from the operation what they will get back.

Beyond the inconsistency, returning NumPy makes reductions a hole in the lazy
model — the result of a reduction over a 100 GB array is materialised eagerly
and cannot be fed back into a blosc2 pipeline without a round trip through
asarray.

Array API

The array API specification has reduction functions return arrays of the calling
namespace rather than language scalars, so blosc2.sum(a) yielding
numpy.float64 is non-compliant regardless of which way the inconsistency above
is resolved. Related to #466, but kept separate: that issue is a checklist
organised by failing test file, whereas this cuts across every reduction and
needs a decision before those boxes can be ticked consistently.

Open questions

1. Which way to converge? Making everything return blosc2 arrays is the
   array-API answer and the consistent one. Making the lazy path return NumPy
   would also be consistent, but gives up the ability to keep a reduction lazy.
2. What about full (0-d) reductions? A full reduction is one number, and
   wrapping it in a compressed container with a schunk, chunks and blocks costs
   more than it returns — every subsequent float() pays a decompression. An
   axis=-reduction is a different matter. A split rule ("arrays for partial,
   scalars for full") would be pragmatic but is exactly what the array API
   forbids.
3. Deprecation. This is a breaking change: if a.sum() > 5, float(a.mean()),
   passing a result to matplotlib or to a C API all change behaviour. It needs a
   transition plan — a keyword, a namespace flag, or a release where both are
   documented — rather than a flag day.

Raised as a follow-up in #457 (whose indexing bug is now fixed), and split out
because it is an API semantics decision rather than a defect.