## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B4', 'milligrams of calcium', 'grams of carbohydrates'], which can be represented symbolically as $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the objective function into a symbolic representation
The objective function to minimize is $9.36x_1^2 + 9.52x_1x_2 + 9.56x_1x_3 + 7.32x_2^2 + 4.46x_2x_3 + 1.98x_3^2 + 1.78x_1 + 7.27x_2 + 4.88x_3$.

## 3: Define the constraints in symbolic notation
The constraints are:
- $6x_1 \leq 102$
- $3x_2 \leq 102$
- $9x_3 \leq 102$
- $6x_1 + 3x_2 \geq 12$
- $6x_1 + 9x_3 \geq 11$
- $6x_1 + 3x_2 + 9x_3 \geq 11$
- $-6x_1^2 + 8x_2^2 \geq 0$
- $6x_1 + 3x_2 + 9x_3 \leq 49$
- $x_1$ is an integer
- $x_2$ is an integer
- $x_3$ is continuous

## 4: Create a symbolic representation of the problem
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B4'), ('x2', 'milligrams of calcium'), ('x3', 'grams of carbohydrates')],
'objective_function': '9.36*x1^2 + 9.52*x1*x2 + 9.56*x1*x3 + 7.32*x2^2 + 4.46*x2*x3 + 1.98*x3^2 + 1.78*x1 + 7.27*x2 + 4.88*x3',
'constraints': [
'6*x1 <= 102',
'3*x2 <= 102',
'9*x3 <= 102',
'6*x1 + 3*x2 >= 12',
'6*x1 + 9*x3 >= 11',
'6*x1 + 3*x2 + 9*x3 >= 11',
'-6*x1^2 + 8*x2^2 >= 0',
'6*x1 + 3*x2 + 9*x3 <= 49',
'x1 % 1 == 0',
'x2 % 1 == 0'
]
}
```

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

def optimize_problem():
    model = gurobi.Model()
    
    # Define variables
    x1 = model.addVar(name="milligrams_of_vitamin_B4", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="milligrams_of_calcium", vtype=gurobi.GRB.INTEGER)
    x3 = model.addVar(name="grams_of_carbohydrates")
    
    # Objective function
    model.setObjective(9.36*x1**2 + 9.52*x1*x2 + 9.56*x1*x3 + 7.32*x2**2 + 4.46*x2*x3 + 1.98*x3**2 + 1.78*x1 + 7.27*x2 + 4.88*x3, gurobi.GRB.MINIMIZE)
    
    # Constraints
    model.addConstr(6*x1 <= 102)
    model.addConstr(3*x2 <= 102)
    model.addConstr(9*x3 <= 102)
    model.addConstr(6*x1 + 3*x2 >= 12)
    model.addConstr(6*x1 + 9*x3 >= 11)
    model.addConstr(6*x1 + 3*x2 + 9*x3 >= 11)
    model.addConstr(-6*x1**2 + 8*x2**2 >= 0)
    model.addConstr(6*x1 + 3*x2 + 9*x3 <= 49)
    
    # Optimize
    model.optimize()
    
    # Print solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Objective: ", model.objVal)
        print("x1: ", x1.varValue)
        print("x2: ", x2.varValue)
        print("x3: ", x3.varValue)
    else:
        print("The model is infeasible")

optimize_problem()
```