Here's the `and.py` file with the `AndCreate` and `And` schemas for the `blog_app` application:

```python
# app/api/v1/schemas/and.py

from typing import Optional
from pydantic import BaseModel

# AndBase schema
class AndBase(BaseModel):
    name: str
    description: Optional[str] = None

# AndCreate schema for creating a new "and" instance
class AndCreate(AndBase):
    pass

# And schema for reading "and" instances
class And(AndBase):
    id: int
    name: str
    description: Optional[str] = None

    class Config:
        orm_mode = True
```

Explanation:

1. We import the necessary modules: `typing` for type hints, and `pydantic` for defining data models.
2. `AndBase` is a base Pydantic model that defines the common fields for both `AndCreate` and `And` schemas. It has two fields: `name` (required) and `description` (optional).
3. `AndCreate` inherits from `AndBase` and represents the schema for creating a new "and" instance. It doesn't add any additional fields.
4. `And` inherits from `AndBase` and represents the schema for reading "and" instances. It adds an `id` field, which is typically the primary key in the database.
5. In the `And` model, we set `orm_mode = True` in the `Config` class. This allows Pydantic to work with ORM objects (e.g., SQLAlchemy models) and automatically convert them to Pydantic models.

This file can be used in your FastAPI application to define request and response models for the "and" entity in the `blog_app` context. You can import these models in your API routes and use them for data validation, serialization, and deserialization.