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

Let's define the symbolic variables:
- $x_1$ represents the number of individual cereal boxes
- $x_2$ represents the number of family size cereal boxes

The objective function to maximize profit is: $4x_1 + 8x_2$

The constraints based on the given resources are:
- $20x_1 + 60x_2 \leq 2000$ (cereal units constraint)
- $10x_1 + 15x_2 \leq 750$ (filling time constraint)
- $x_1 \geq 0$ and $x_2 \geq 0$ (non-negativity constraints)

## Step 2: Convert the symbolic representation into a Gurobi code

We will use the Gurobi library in Python to solve this linear programming problem.

## Step 3: Write the Gurobi code

```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="individual_boxes", lb=0, ub=None)
    x2 = model.addVar(name="family_size_boxes", lb=0, ub=None)

    # Define the objective function
    model.setObjective(4 * x1 + 8 * x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(20 * x1 + 60 * x2 <= 2000, name="cereal_constraint")
    model.addConstr(10 * x1 + 15 * x2 <= 750, name="time_constraint")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Number of individual cereal boxes: {x1.varValue}")
        print(f"Number of family size cereal boxes: {x2.varValue}")
        print(f"Maximal profit: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_cereal_problem()
```

## Step 4: Provide the symbolic representation in JSON format

```json
{
    'sym_variables': [('x1', 'individual cereal boxes'), ('x2', 'family size cereal boxes')],
    'objective_function': '4*x1 + 8*x2',
    'constraints': [
        '20*x1 + 60*x2 <= 2000',
        '10*x1 + 15*x2 <= 750',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```