## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:

- `x1`: tables
- `x2`: chairs

The objective function to maximize profit is:
- Profit from tables: $1000*x1$
- Profit from chairs: $300*x2$
- Total profit: $1000*x1 + 300*x2$

The constraints based on the given resources are:
- Crafting hours: $5*x1 + 2*x2 \leq 25$
- Polishing hours: $2*x1 + 1*x2 \leq 15$
- Non-negativity constraints: $x1 \geq 0, x2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'tables'), ('x2', 'chairs')],
    'objective_function': '1000*x1 + 300*x2',
    'constraints': [
        '5*x1 + 2*x2 <= 25',
        '2*x1 + x2 <= 15',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobi

def woodshop_production_planning():
    # Create a new model
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="tables", lb=0, ub=gurobi.GRB.INFINITY)
    x2 = model.addVar(name="chairs", lb=0, ub=gurobi.GRB.INFINITY)

    # Objective function: maximize profit
    model.setObjective(1000*x1 + 300*x2, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(5*x1 + 2*x2 <= 25, name="crafting_hours")
    model.addConstr(2*x1 + x2 <= 15, name="polishing_hours")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Tables: {x1.varValue}")
        print(f"Chairs: {x2.varValue}")
        print(f"Max Profit: {model.objVal}")
    else:
        print("No optimal solution found.")

woodshop_production_planning()
```