## Symbolic Representation

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

Let's define the variables:
- $x_1$ : nutritional cereal
- $x_2$ : kids' cereal
- $x_3$ : sugary cereal

The objective is to maximize revenue, given by:
\[ \text{Maximize:} \quad 1x_1 + 1.5x_2 + 2x_3 \]

Subject to the constraints:
- $3x_1 + 1.5x_2 + 2x_3 \leq 500$ (oat constraint)
- $1x_1 + 1.5x_2 + 4x_3 \leq 700$ (sugar constraint)
- $x_1, x_2, x_3 \geq 0$ (non-negativity constraint)

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [
        ('x1', 'nutritional cereal'),
        ('x2', 'kids\' cereal'),
        ('x3', 'sugary cereal')
    ],
    'objective_function': '1*x1 + 1.5*x2 + 2*x3',
    'constraints': [
        '3*x1 + 1.5*x2 + 2*x3 <= 500',
        '1*x1 + 1.5*x2 + 4*x3 <= 700',
        'x1 >= 0',
        'x2 >= 0',
        'x3 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="nutritional_cereal", lb=0)
x2 = model.addVar(name="kids_cereal", lb=0)
x3 = model.addVar(name="sugary_cereal", lb=0)

# Define the objective function
model.setObjective(1*x1 + 1.5*x2 + 2*x3, gp.GRB.MAXIMIZE)

# Add the constraints
model.addConstr(3*x1 + 1.5*x2 + 2*x3 <= 500, name="oat_constraint")
model.addConstr(1*x1 + 1.5*x2 + 4*x3 <= 700, name="sugar_constraint")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Nutritional Cereal: {x1.varValue}")
    print(f"Kids' Cereal: {x2.varValue}")
    print(f"Sugary Cereal: {x3.varValue}")
    print(f"Max Revenue: {model.objVal}")
else:
    print("The model is infeasible or unbounded.")
```