Design Pydantic v2 models that validate a real domain, not just shapes

Design Pydantic v2 models for the domain below. The goal is models that reject bad data at the boundary, not models that just describe field names and types.

DOMAIN
A booking for a rental property, plus the guest making it

BUSINESS RULES TO ENFORCE
check-out date must be after check-in date; number of guests can't exceed the property's max_occupancy; a booking under 2 nights requires a minimum-stay override flag

REQUIREMENTS
1. Use Pydantic v2 syntax specifically: model_config = ConfigDict(...), not the v1-style class Config. Use Field(...) constraints (gt, max_length, pattern, etc.) for anything checkable declaratively, before reaching for a custom validator.
2. Use @field_validator for single-field logic that Field() can't express, and @model_validator(mode="after") for anything depending on more than one field (e.g. end_date must be after start_date). Name which business rule each validator enforces, in a comment.
3. Be deliberate about Optional versus required — a field should only be optional if the domain genuinely allows it to be absent, not because it's convenient during construction. Justify each optional field in one line.
4. Use Annotated[...] types for any constraint you'd otherwise repeat across multiple models, instead of copy-pasting the same Field(...) arguments everywhere.
5. Serialization: API is camelCase JSON (checkInDate), Python code should stay snake_case. If any field needs a different name on the wire than in Python, use alias or an AliasGenerator and set populate_by_name correctly, rather than manually renaming keys after the fact.
6. Give every model a docstring stating what real-world entity it represents and what invalid state it's specifically guarding against.

OUTPUT FORMAT
1. The models, in dependency order.
2. A short table: business rule, and which model or validator enforces it.
3. One example payload that should raise ValidationError, and what the error tells the caller.