## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin A' and 'grams of fiber', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is $7.92x_1^2 + 4.18x_1 + 2.24x_2$.

## Step 3: List the constraints in symbolic notation
The constraints given are:
- $2x_1 \leq 141$
- $7x_1 \leq 68$
- $2x_1 \leq 152$
- $8x_2 \leq 141$
- $4x_2 \leq 68$
- $x_2 \leq 152$
- $2x_1^2 + 8x_2^2 \geq 53$
- $7x_1 + 4x_2 \geq 16$
- $2x_1 + x_2 \geq 72$
- $7x_1 - 3x_2 \geq 0$
- $2x_1^2 + 8x_2^2 \leq 80$
- $2x_1 + 8x_2 \leq 80$
- $7x_1 + 4x_2 \leq 46$
- $2x_1 + x_2 \leq 85$

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of vitamin A'), ('x2', 'grams of fiber')],
'objective_function': '7.92*x1^2 + 4.18*x1 + 2.24*x2',
'constraints': [
    '2*x1 <= 141',
    '7*x1 <= 68',
    '2*x1 <= 152',
    '8*x2 <= 141',
    '4*x2 <= 68',
    'x2 <= 152',
    '2*x1^2 + 8*x2^2 >= 53',
    '7*x1 + 4*x2 >= 16',
    '2*x1 + x2 >= 72',
    '7*x1 - 3*x2 >= 0',
    '2*x1^2 + 8*x2^2 <= 80',
    '2*x1 + 8*x2 <= 80',
    '7*x1 + 4*x2 <= 46',
    '2*x1 + x2 <= 85'
]
}
```

## 5: Write the Gurobi code for the optimization problem
```python
import gurobi

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams_of_vitamin_A")
    x2 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="grams_of_fiber")
    
    # Objective function
    model.setObjective(7.92*x1**2 + 4.18*x1 + 2.24*x2, gurobi.GRB.MAXIMIZE)
    
    # Constraints
    model.addConstr(2*x1 <= 141)
    model.addConstr(7*x1 <= 68)
    model.addConstr(2*x1 <= 152)
    model.addConstr(8*x2 <= 141)
    model.addConstr(4*x2 <= 68)
    model.addConstr(x2 <= 152)
    model.addConstr(2*x1**2 + 8*x2**2 >= 53)
    model.addConstr(7*x1 + 4*x2 >= 16)
    model.addConstr(2*x1 + x2 >= 72)
    model.addConstr(7*x1 - 3*x2 >= 0)
    model.addConstr(2*x1**2 + 8*x2**2 <= 80)
    model.addConstr(2*x1 + 8*x2 <= 80)
    model.addConstr(7*x1 + 4*x2 <= 46)
    model.addConstr(2*x1 + x2 <= 85)
    
    # Optimize
    model.optimize()
    
    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin A: {x1.varValue}")
        print(f"Grams of fiber: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```