## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'milligrams of potassium'
- $x_1$ represents 'grams of carbohydrates'
- $x_2$ represents 'grams of fiber'
- $x_3$ represents 'milligrams of vitamin D'

## Step 2: Convert the objective function into a symbolic representation
The objective function to maximize is: 
$9.98x_0^2 + 7.69x_0x_1 + 5.3x_0x_3 + 3.94x_1x_2 + 4.62x_2^2 + 2.16x_3^2 + 4.23x_1 + 7.64x_2$

## 3: List the constraints in symbolic notation
Constraints:
1. $10x_0 \leq 132$
2. $8x_0 \leq 142$
3. $10x_0 \leq 47$
4. $14x_0 \leq 124$
5. $4x_1 \leq 132$
6. $3x_1 \leq 142$
7. $14x_1 \leq 47$
8. $8x_1 \leq 124$
9. $10x_2 \leq 132$
10. $4x_2 \leq 142$
11. $6x_2 \leq 47$
12. $10x_2 \leq 124$
13. $2x_3 \leq 132$
14. $3x_3 \leq 142$
15. $x_3 \leq 47$
16. $12x_3 \leq 124$
17. $4x_1 + 10x_2 \geq 12$
18. $10x_0 + 4x_1 \geq 25$
19. $3x_1 + 4x_2 \geq 21$
20. $3x_1 + 3x_3 \geq 17$
21. $8x_0 + 4x_2 \geq 32$
22. $8x_0 + 3x_1 + 4x_2 \geq 28$
23. $10x_0 + 6x_2 \geq 11$
24. $100x_0^2 + 196x_1^2 \geq 10$
25. $6x_2 + x_3 \geq 11$
26. $14x_1 + x_3 \geq 11$
27. $196x_0^2 + 144x_3^2 \geq 14$
28. $10x_2 + x_3 \geq 20$
29. $8x_1 + 10x_2 \geq 25$
30. $196x_0^2 + 196x_1^2 \geq 31$
31. $16x_1^2 + 100x_2^2 \leq 102$
32. $10x_2 + 2x_3 \leq 49$
33. $100x_0^2 + 144x_3^2 \leq 87$
34. $10x_0 + 10x_2 \leq 120$
35. $4x_1 + 2x_3 \leq 35$
36. $100x_0^2 + 196x_1^2 \leq 62$
37. $100x_0^2 + 196x_1^2 + 144x_3^2 \leq 110$
38. $16x_1^2 + 100x_2^2 + 144x_3^2 \leq 33$
39. $100x_0^2 + 196x_1^2 + 100x_2^2 \leq 46$
40. $10x_0 + 4x_1 + 10x_2 + 2x_3 \leq 46$
41. $3x_1 + 4x_2 \leq 38$
42. $16x_2^2 + 144x_3^2 \leq 120$
43. $3x_1 + 3x_3 \leq 66$
44. $8x_0 + 4x_2 \leq 106$
45. $8x_0 + 3x_1 + 4x_2 + 3x_3 \leq 106$
46. $196x_1^2 + 144x_3^2 \leq 36$
47. $10x_0 + x_3 \leq 26$
48. $14x_1 + 6x_2 + x_3 \leq 26$
49. $8x_0 + 3x_1 + 4x_2 + 3x_3 \leq 26$
50. $16x_1^2 + 100x_2^2 \leq 36$
51. $196x_0^2 + 144x_3^2 \leq 108$
52. $8x_0 + 3x_1 + 10x_2 + 3x_3 \leq 108$

## 4: Define the optimization problem in Gurobi
```python
import gurobi as gp

# Define the model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", vtype=gp.GRB.INTEGER)  # milligrams of potassium
x1 = m.addVar(name="x1")  # grams of carbohydrates
x2 = m.addVar(name="x2", vtype=gp.GRB.INTEGER)  # grams of fiber
x3 = m.addVar(name="x3", vtype=gp.GRB.INTEGER)  # milligrams of vitamin D

# Objective function
m.setObjective(9.98 * x0**2 + 7.69 * x0 * x1 + 5.3 * x0 * x3 + 3.94 * x1 * x2 + 4.62 * x2**2 + 2.16 * x3**2 + 4.23 * x1 + 7.64 * x2, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(10 * x0 <= 132)
m.addConstr(8 * x0 <= 142)
m.addConstr(10 * x0 <= 47)
m.addConstr(14 * x0 <= 124)
m.addConstr(4 * x1 <= 132)
m.addConstr(3 * x1 <= 142)
m.addConstr(14 * x1 <= 47)
m.addConstr(8 * x1 <= 124)
m.addConstr(10 * x2 <= 132)
m.addConstr(4 * x2 <= 142)
m.addConstr(6 * x2 <= 47)
m.addConstr(10 * x2 <= 124)
m.addConstr(2 * x3 <= 132)
m.addConstr(3 * x3 <= 142)
m.addConstr(x3 <= 47)
m.addConstr(12 * x3 <= 124)
m.addConstr(4 * x1 + 10 * x2 >= 12)
m.addConstr(10 * x0 + 4 * x1 >= 25)
m.addConstr(3 * x1 + 4 * x2 >= 21)
m.addConstr(3 * x1 + 3 * x3 >= 17)
m.addConstr(8 * x0 + 4 * x2 >= 32)
m.addConstr(8 * x0 + 3 * x1 + 4 * x2 >= 28)
m.addConstr(10 * x0 + 6 * x2 >= 11)
m.addConstr(100 * x0**2 + 196 * x1**2 >= 10)
m.addConstr(6 * x2 + x3 >= 11)
m.addConstr(14 * x1 + x3 >= 11)
m.addConstr(196 * x0**2 + 144 * x3**2 >= 14)
m.addConstr(10 * x2 + x3 >= 20)
m.addConstr(8 * x1 + 10 * x2 >= 25)
m.addConstr(196 * x0**2 + 196 * x1**2 >= 31)
m.addConstr(16 * x1**2 + 100 * x2**2 <= 102)
m.addConstr(10 * x2 + 2 * x3 <= 49)
m.addConstr(100 * x0**2 + 144 * x3**2 <= 87)
m.addConstr(10 * x0 + 10 * x2 <= 120)
m.addConstr(4 * x1 + 2 * x3 <= 35)
m.addConstr(100 * x0**2 + 196 * x1**2 <= 62)
m.addConstr(100 * x0**2 + 196 * x1**2 + 144 * x3**2 <= 110)
m.addConstr(16 * x1**2 + 100 * x2**2 + 144 * x3**2 <= 33)
m.addConstr(100 * x0**2 + 196 * x1**2 + 100 * x2**2 <= 46)
m.addConstr(10 * x0 + 4 * x1 + 10 * x2 + 2 * x3 <= 46)
m.addConstr(3 * x1 + 4 * x2 <= 38)
m.addConstr(16 * x2**2 + 144 * x3**2 <= 120)
m.addConstr(3 * x1 + 3 * x3 <= 66)
m.addConstr(8 * x0 + 4 * x2 <= 106)
m.addConstr(8 * x0 + 3 * x1 + 4 * x2 + 3 * x3 <= 106)
m.addConstr(196 * x1**2 + 144 * x3**2 <= 36)
m.addConstr(10 * x0 + x3 <= 26)
m.addConstr(14 * x1 + 6 * x2 + x3 <= 26)
m.addConstr(8 * x0 + 3 * x1 + 4 * x2 + 3 * x3 <= 26)
m.addConstr(16 * x1**2 + 100 * x2**2 <= 36)
m.addConstr(196 * x0**2 + 144 * x3**2 <= 108)
m.addConstr(8 * x0 + 3 * x1 + 10 * x2 + 3 * x3 <= 108)

# Solve the model
m.optimize()

# Print the solution
if m.status == gp.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("milligrams of potassium:", x0.varValue)
    print("grams of carbohydrates:", x1.varValue)
    print("grams of fiber:", x2.varValue)
    print("milligrams of vitamin D:", x3.varValue)
else:
    print("No optimal solution found.")
```

```json
{
    'sym_variables': [
        ('x0', 'milligrams of potassium'), 
        ('x1', 'grams of carbohydrates'), 
        ('x2', 'grams of fiber'), 
        ('x3', 'milligrams of vitamin D')
    ], 
    'objective_function': '9.98*x0^2 + 7.69*x0*x1 + 5.3*x0*x3 + 3.94*x1*x2 + 4.62*x2^2 + 2.16*x3^2 + 4.23*x1 + 7.64*x2', 
    'constraints': [
        '10*x0 <= 132', 
        '8*x0 <= 142', 
        '10*x0 <= 47', 
        '14*x0 <= 124', 
        '4*x1 <= 132', 
        '3*x1 <= 142', 
        '14*x1 <= 47', 
        '8*x1 <= 124', 
        '10*x2 <= 132', 
        '4*x2 <= 142', 
        '6*x2 <= 47', 
        '10*x2 <= 124', 
        '2*x3 <= 132', 
        '3*x3 <= 142', 
        'x3 <= 47', 
        '12*x3 <= 124', 
        '4*x1 + 10*x2 >= 12', 
        '10*x0 + 4*x1 >= 25', 
        '3*x1 + 4*x2 >= 21', 
        '3*x1 + 3*x3 >= 17', 
        '8*x0 + 4*x2 >= 32', 
        '8*x0 + 3*x1 + 4*x2 >= 28', 
        '10*x0 + 6*x2 >= 11', 
        '100*x0^2 + 196*x1^2 >= 10', 
        '6*x2 + x3 >= 11', 
        '14*x1 + x3 >= 11', 
        '196*x0^2 + 144*x3^2 >= 14', 
        '10*x2 + x3 >= 20', 
        '8*x1 + 10*x2 >= 25', 
        '196*x0^2 + 196*x1^2 >= 31', 
        '16*x1^2 + 100*x2^2 <= 102', 
        '10*x2 + 2*x3 <= 49', 
        '100*x0^2 + 144*x3^2 <= 87', 
        '10*x0 + 10*x2 <= 120', 
        '4*x1 + 2*x3 <= 35', 
        '100*x0^2 + 196*x1^2 <= 62', 
        '100*x0^2 + 196*x1^2 + 144*x3^2 <= 110', 
        '16*x1^2 + 100*x2^2 + 144*x3^2 <= 33', 
        '100*x0^2 + 196*x1^2 + 100*x2^2 <= 46', 
        '10*x0 + 4*x1 + 10*x2 + 2*x3 <= 46', 
        '3*x1 + 4*x2 <= 38', 
        '16*x2^2 + 144*x3^2 <= 120', 
        '3*x1 + 3*x3 <= 66', 
        '8*x0 + 4*x2 <= 106', 
        '8*x0 + 3*x1 + 4*x2 + 3*x3 <= 106', 
        '196*x1^2 + 144*x3^2 <= 36', 
        '10*x0 + x3 <= 26', 
        '14*x1 + 6*x2 + x3 <= 26', 
        '8*x0 + 3*x1 + 4*x2 + 3*x3 <= 26', 
        '16*x1^2 + 100*x2^2 <= 36', 
        '196*x0^2 + 144*x3^2 <= 108', 
        '8*x0 + 3*x1 + 10*x2 + 3*x3 <= 108'
    ]
}
```