## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B7', 'milligrams of vitamin B12', 'milligrams of zinc', 'milligrams of vitamin B2'] which can be represented symbolically as $x_0, x_1, x_2, x_3$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin B7
- $x_1$ : milligrams of vitamin B12
- $x_2$ : milligrams of zinc
- $x_3$ : milligrams of vitamin B2

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $7.67x_0^2 + 1.72x_0x_1 + 8.57x_0x_3 + 8.81x_1^2 + 2.9x_1x_2 + 7.6x_2^2 + 1.36x_2x_3 + 5.69x_0 + 5.34x_2$

## 4: List the constraints in symbolic notation
Constraints:
1. $10x_0 \leq 83$
2. $9x_0 \leq 312$
3. $11x_1 \leq 83$
4. $5x_1 \leq 312$
5. $13x_2 \leq 83$
6. $10x_2 \leq 312$
7. $12x_3 \leq 83$
8. $11x_3 \leq 312$
9. $11x_1 + 13x_2 \geq 14$
10. $11x_1 + 12x_3 \geq 10$
11. $10x_0^2 + 13x_2^2 \geq 8$
12. $13x_2 + 12x_3 \geq 8$
13. $10x_0 + 11x_1 \geq 12$
14. $9x_0 + 5x_1 + 10x_2 \geq 67$
15. $9x_0 + 5x_1 + 11x_3 \geq 67$
16. $9x_0^2 + 5x_1^2 + 10x_2^2 \geq 49$
17. $9x_0 + 5x_1 + 11x_3 \geq 49$
18. $10x_0 + 11x_1 \leq 48$
19. $10x_0^2 + 13x_2^2 \leq 31$
20. $10x_0 + 12x_3 \leq 60$
21. $11x_1 + 13x_2 \leq 42$
22. $10x_0 + 11x_1 + 13x_2 + 12x_3 \leq 42$
23. $9x_0^2 + 11x_3^2 \leq 194$
24. $9x_0 + 5x_1 \leq 228$
25. $9x_0^2 + 10x_2^2 \leq 195$
26. $5x_1^2 + 10x_2^2 + 11x_3^2 \leq 194$
27. $9x_0 + 5x_1 + 11x_3 \leq 141$
28. $9x_0 + 5x_1 + 10x_2 + 11x_3 \leq 141$

## 5: Define the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B7'), 
        ('x1', 'milligrams of vitamin B12'), 
        ('x2', 'milligrams of zinc'), 
        ('x3', 'milligrams of vitamin B2')
    ], 
    'objective_function': '7.67*x0^2 + 1.72*x0*x1 + 8.57*x0*x3 + 8.81*x1^2 + 2.9*x1*x2 + 7.6*x2^2 + 1.36*x2*x3 + 5.69*x0 + 5.34*x2', 
    'constraints': [
        '10*x0 <= 83', 
        '9*x0 <= 312', 
        '11*x1 <= 83', 
        '5*x1 <= 312', 
        '13*x2 <= 83', 
        '10*x2 <= 312', 
        '12*x3 <= 83', 
        '11*x3 <= 312', 
        '11*x1 + 13*x2 >= 14', 
        '11*x1 + 12*x3 >= 10', 
        '10*x0^2 + 13*x2^2 >= 8', 
        '13*x2 + 12*x3 >= 8', 
        '10*x0 + 11*x1 >= 12', 
        '9*x0 + 5*x1 + 10*x2 >= 67', 
        '9*x0 + 5*x1 + 11*x3 >= 67', 
        '9*x0^2 + 5*x1^2 + 10*x2^2 >= 49', 
        '9*x0 + 5*x1 + 11*x3 >= 49', 
        '10*x0 + 11*x1 <= 48', 
        '10*x0^2 + 13*x2^2 <= 31', 
        '10*x0 + 12*x3 <= 60', 
        '11*x1 + 13*x2 <= 42', 
        '10*x0 + 11*x1 + 13*x2 + 12*x3 <= 42', 
        '9*x0^2 + 11*x3^2 <= 194', 
        '9*x0 + 5*x1 <= 228', 
        '9*x0^2 + 10*x2^2 <= 195', 
        '5*x1^2 + 10*x2^2 + 11*x3^2 <= 194', 
        '9*x0 + 5*x1 + 11*x3 <= 141', 
        '9*x0 + 5*x1 + 10*x2 + 11*x3 <= 141'
    ]
}
```

## 6: Implement the optimization problem using Gurobi
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name="x0", lb=-float('inf'), ub=float('inf'))  # milligrams of vitamin B7
x1 = model.addVar(name="x1", lb=-float('inf'), ub=float('inf'))  # milligrams of vitamin B12
x2 = model.addVar(name="x2", lb=0, ub=float('inf'), vtype=gurobi.GRB.INTEGER)  # milligrams of zinc
x3 = model.addVar(name="x3", lb=-float('inf'), ub=float('inf'), vtype=gurobi.GRB.INTEGER)  # milligrams of vitamin B2

# Objective function
model.setObjective(7.67*x0**2 + 1.72*x0*x1 + 8.57*x0*x3 + 8.81*x1**2 + 2.9*x1*x2 + 7.6*x2**2 + 1.36*x2*x3 + 5.69*x0 + 5.34*x2, gurobi.GRB.MAXIMIZE)

# Constraints
model.addConstr(10*x0 <= 83)
model.addConstr(9*x0 <= 312)
model.addConstr(11*x1 <= 83)
model.addConstr(5*x1 <= 312)
model.addConstr(13*x2 <= 83)
model.addConstr(10*x2 <= 312)
model.addConstr(12*x3 <= 83)
model.addConstr(11*x3 <= 312)
model.addConstr(11*x1 + 13*x2 >= 14)
model.addConstr(11*x1 + 12*x3 >= 10)
model.addConstr(10*x0**2 + 13*x2**2 >= 8)
model.addConstr(13*x2 + 12*x3 >= 8)
model.addConstr(10*x0 + 11*x1 >= 12)
model.addConstr(9*x0 + 5*x1 + 10*x2 >= 67)
model.addConstr(9*x0 + 5*x1 + 11*x3 >= 67)
model.addConstr(9*x0**2 + 5*x1**2 + 10*x2**2 >= 49)
model.addConstr(9*x0 + 5*x1 + 11*x3 >= 49)
model.addConstr(10*x0 + 11*x1 <= 48)
model.addConstr(10*x0**2 + 13*x2**2 <= 31)
model.addConstr(10*x0 + 12*x3 <= 60)
model.addConstr(11*x1 + 13*x2 <= 42)
model.addConstr(10*x0 + 11*x1 + 13*x2 + 12*x3 <= 42)
model.addConstr(9*x0**2 + 11*x3**2 <= 194)
model.addConstr(9*x0 + 5*x1 <= 228)
model.addConstr(9*x0**2 + 10*x2**2 <= 195)
model.addConstr(5*x1**2 + 10*x2**2 + 11*x3**2 <= 194)
model.addConstr(9*x0 + 5*x1 + 11*x3 <= 141)
model.addConstr(9*x0 + 5*x1 + 10*x2 + 11*x3 <= 141)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.Status.OPTIMAL:
    print("Objective: ", model.objVal)
    print("x0: ", x0.varValue)
    print("x1: ", x1.varValue)
    print("x2: ", x2.varValue)
    print("x3: ", x3.varValue)
else:
    print("The model is infeasible")
```