## Step 1: Define the symbolic representation of the problem

Let's denote:
- $x_1$ as the number of model trains
- $x_2$ as the number of model planes

The objective is to maximize profit $P = 7x_1 + 9x_2$.

## Step 2: Identify the constraints

1. Building time constraint: $30x_1 + 40x_2 \leq 5000$
2. Painting time constraint: $40x_1 + 50x_2 \leq 6000$
3. Non-negativity constraint: $x_1 \geq 0, x_2 \geq 0$

## Step 3: Symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'model trains'), ('x2', 'model planes')],
    'objective_function': '7*x1 + 9*x2',
    'constraints': [
        '30*x1 + 40*x2 <= 5000',
        '40*x1 + 50*x2 <= 6000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Step 4: Convert the problem into Gurobi code

```python
import gurobi

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

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

    # Define objective function
    model.setObjective(7 * x1 + 9 * x2, gurobi.GRB.MAXIMIZE)

    # Add constraints
    model.addConstr(30 * x1 + 40 * x2 <= 5000, name="building_time")
    model.addConstr(40 * x1 + 50 * x2 <= 6000, name="painting_time")

    # Optimize model
    model.optimize()

    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print(f"Optimal solution: {x1.varName} = {x1.x}, {x2.varName} = {x2.x}")
        print(f"Maximum profit: ${model.objVal:.2f}")
    else:
        print("No optimal solution found")

solve_model_trains_and_planes()
```