fix: Bound embedding input length and attention memory per batch

[buildingjoshbetter]

Embedding models were run with their full input limit (32,768 tokens for Qwen3-Embedding-0.6B) and a fixed 32-text batch. Attention memory grows with batch × length², so one batch of long texts could need tens of GB. On a Mac it fails on MPS first and is then retried on CPU. This applies the same dynamic bound as the reranker change (#836) to every embedding path and tier.

• Caps embedder input at 2,048 tokens. That is above the longest stored memory, so existing vectors do not change; only oversized texts such as pasted documents are truncated.
• Embeds texts longest-first in batches sized so batch × tokens² stays within the cost of 32 texts of 1,024 tokens. The budget is halved below 6 GiB of available memory and quartered below 2 GiB.
• Keeps the original single encode call, with no measurement, for single texts, tensor or list outputs, and batches that fit even at the smallest budget.
• Applies to every SentenceTransformer embedder: base/pro Qwen3, legacy MiniLM/bge-small (already below the cap) and the custom tier. The edge static model2vec embedder is untouched.
• Covers every construction site (local, prepared target, model server, custom), the local encode wrappers and the model server embed loop. The single-query fast lane is bounded by the cap.

Real Qwen3-Embedding-0.6B on CPU, 32 synthetic texts per call, 16 GiB container, peak cgroup memory before → after:

Text length	Before	After	Vectors
50 words	1.50 GB, 0.83 s	0.66 GB, 0.59 s	identical
800 words	1.32 GB, 23.1 s	1.32 GB, 23.5 s	identical
3,000 words	3.82 GB, 129 s	1.03 GB, 61 s	change (2,048-token cap)
12,000 words	killed at 16 GiB	1.05 GB, 61 s	before never completed

Single-query latency: 30.77 ms → 30.87 ms median over 200 calls (within noise).

Validation at aa37923, stacked on #836:

• Combined suite: 4,529 tests / 5,048 subtests passed (14 new tests), 19 skips, 3 network cases deselected.
• Focused suites: 782 / 2,336 and 1,327 / 1,469 passed.
• Installed wheel smoke: 95 Python source files verified.
• Separate original gates: 739 native source/inert cases and 276 memory cases passed.
• Independent review accepted.

tests/test-model-daemon-admission-297.py pins the exact bytes of mps_utils.py to guard against unintended edits. The pin is updated to the reviewed bytes; the mps_utils.py change is the three guarded budgeted_encode call sites.

Base is maintenance/reranker-token-budget-v1 (#836). Draft; no merges or releases.