## Symbolic Representation

To solve the problem described, we first need to convert the natural language description into a symbolic representation. Let's denote:

- \(x_1\) as the number of superhero figurines
- \(x_2\) as the number of cartoon figurines

The objective is to maximize profit, with a profit of $10 per superhero figurine and $8 per cartoon figurine. Therefore, the objective function can be represented as:

\[ \text{Maximize:} \quad 10x_1 + 8x_2 \]

The constraints based on the machine hours available are:

- Printer A: \(5x_1 + 3x_2 \leq 1000\)
- Printer B: \(4x_1 + 7x_2 \leq 1000\)

Also, \(x_1 \geq 0\) and \(x_2 \geq 0\), since the number of figurines cannot be negative.

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'superhero figurines'), ('x2', 'cartoon figurines')],
    'objective_function': '10*x1 + 8*x2',
    'constraints': [
        '5*x1 + 3*x2 <= 1000',
        '4*x1 + 7*x2 <= 1000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

# Create a new model
model = gp.Model("Figurine Production")

# Define variables
x1 = model.addVar(name="superhero_figurines", lb=0, vtype=gp.GRB.INTEGER)
x2 = model.addVar(name="cartoon_figurines", lb=0, vtype=gp.GRB.INTEGER)

# Objective function: Maximize profit
model.setObjective(10*x1 + 8*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(5*x1 + 3*x2 <= 1000, name="Printer_A_constraint")
model.addConstr(4*x1 + 7*x2 <= 1000, name="Printer_B_constraint")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal solution: {model.x[0]} superhero figurines, {model.x[1]} cartoon figurines")
    print(f"Max profit: ${model.objVal}")
else:
    print("No optimal solution found")
```