refactor(realism): move emailgen LLM/personas/prompt into shared library

Lift the format-agnostic pieces from decnet/orchestrator/emailgen/
into the new decnet/realism/ library so file-class content generation
(stage 3 of the realism migration) can reuse them. Email-specific
delivery (RFC 2822 EML, IMAP/POP3 spool, thread chains) stays in
orchestrator/.

Renames (history-preserving git mv):
  emailgen/personas.py     -> realism/personas.py
  emailgen/prompt.py       -> realism/prompts/email.py
  emailgen/global_pool.py  -> realism/personas_pool.py
  emailgen/llm/            -> realism/llm/

Env-var clean break (pre-v1, no aliases):
  DECNET_EMAILGEN_LLM      -> DECNET_REALISM_LLM
  DECNET_EMAILGEN_MODEL    -> DECNET_REALISM_MODEL
  DECNET_EMAILGEN_TIMEOUT  -> DECNET_REALISM_TIMEOUT
  DECNET_EMAILGEN_PERSONAS -> DECNET_REALISM_PERSONAS
  DECNET_EMAILGEN_FAKE_OUTPUT -> DECNET_REALISM_FAKE_OUTPUT

Importers rewritten in: orchestrator/emailgen/scheduler.py,
orchestrator/drivers/email.py, web/router/{emailgen,topology}/
api_personas.py, cli/emailgen.py. Tests for moved modules relocated
to tests/realism/; tests for stay-put modules updated in place.

API URL `/api/v1/emailgen/personas` and CLI `decnet emailgen
import-personas` keep their public names until the service-collapse
commit (stage 5).

decnet/realism/llm/impl/fake.py

@@ -0,0 +1,50 @@
+"""In-process fake backend for tests.
+
+Returns a canned string so the driver path can be exercised without an
+Ollama install.  Configurable via ``DECNET_REALISM_FAKE_OUTPUT`` (env)
+or the ``output`` constructor arg — the env-var path lets integration
+tests run the worker end-to-end with deterministic output.
+"""
+from __future__ import annotations
+
+import os
+import time
+from typing import Optional
+
+from decnet.realism.llm.base import LLMBackend, LLMResult
+
+
+_DEFAULT_OUTPUT = (
+    "Subject: Quick update\n\n"
+    "Hi,\n\nFollowing up on the topic.\n\nBest regards,\nFake Persona\n"
+)
+
+
+class FakeBackend(LLMBackend):
+    def __init__(
+        self,
+        *,
+        model: str = "fake-model",
+        timeout: float = 1.0,
+        output: Optional[str] = None,
+        success: bool = True,
+    ) -> None:
+        self.model = model
+        self.timeout = timeout
+        self._output = (
+            output
+            if output is not None
+            else os.environ.get("DECNET_REALISM_FAKE_OUTPUT", _DEFAULT_OUTPUT)
+        )
+        self._success = success
+
+    async def generate(self, prompt: str) -> LLMResult:    # noqa: ARG002
+        t0 = time.monotonic()
+        latency_ms = int((time.monotonic() - t0) * 1000)
+        return LLMResult(
+            success=self._success,
+            text=self._output if self._success else "",
+            model=self.model,
+            latency_ms=latency_ms,
+            extra={"rc": 0 if self._success else 1},
+        )