## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of calcium', 'milligrams of zinc', 'milligrams of vitamin B1', 'milligrams of vitamin B5']. Let's denote them as $x_0$, $x_1$, $x_2$, and $x_3$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of calcium
- $x_1$ : milligrams of zinc
- $x_2$ : milligrams of vitamin B1
- $x_3$ : milligrams of vitamin B5

## 3: Define the objective function in symbolic notation
The objective function to maximize is: $5.08x_0x_1 + 6.35x_1^2 + 6.26x_1x_2 + 6.59x_1x_3 + 3.56x_2^2$.

## 4: List the constraints in symbolic notation
Constraints:
1. $3.53x_0 \leq 376$
2. $15.02x_0 \leq 184$
3. $10.75x_1 \leq 376$
4. $13.63x_1 \leq 184$
5. $6.16x_2 \leq 376$
6. $8.91x_2 \leq 184$
7. $0.26x_3 \leq 376$
8. $3.1x_3 \leq 184$
9. $6.16x_2 + 0.26x_3 \geq 81$
10. $3.53^2x_0^2 + 10.75^2x_1^2 \geq 58$
11. $15.02x_0 + 13.63x_1 + 3.1x_3 \geq 40$
12. $15.02x_0 + 13.63x_1 + 8.91x_2 \geq 40$
13. $13.63x_1 + 8.91x_2 + 3.1x_3 \geq 40$
14. $15.02^2x_0^2 + 13.63^2x_1^2 + 3.1^2x_3^2 \geq 24$
15. $15.02x_0 + 13.63x_1 + 8.91x_2 \geq 24$
16. $13.63^2x_1^2 + 8.91^2x_2^2 + 3.1^2x_3^2 \geq 24$
17. $15.02x_0 + 13.63x_1 + 3.1x_3 \geq 33$
18. $15.02x_0 + 13.63x_1 + 8.91x_2 \geq 33$
19. $13.63x_1 + 8.91x_2 + 3.1x_3 \geq 33$
20. $3.53^2x_0^2 + 10.75^2x_1^2 \leq 186$
21. $10.75x_1 + 0.26x_3 \leq 136$
22. $3.53x_0 + 6.16x_2 \leq 190$
23. $3.53^2x_0^2 + 6.16^2x_2^2 + 0.26^2x_3^2 \leq 370$
24. $3.53x_0 + 10.75x_1 + 6.16x_2 + 0.26x_3 \leq 370$
25. $15.02^2x_0^2 + 3.1^2x_3^2 \leq 159$
26. $8.91x_2 + 3.1x_3 \leq 142$
27. $15.02^2x_0^2 + 8.91^2x_2^2 \leq 50$
28. $3.53^2x_0^2 + 10.75^2x_1^2 \leq 147$
29. $3.53^2x_0^2 + 10.75^2x_1^2 + 0.26^2x_3^2 \leq 120$
30. $3.53^2x_0^2 + 10.75^2x_1^2 + 6.16^2x_2^2 \leq 102$
31. $15.02^2x_0^2 + 8.91^2x_2^2 + 3.1^2x_3^2 \leq 78$
32. $15.02x_0 + 13.63x_1 + 3.1x_3 \leq 78$
33. $x_3$ is an integer.

## 5: Provide the symbolic representation in JSON format
```json
{
    'sym_variables': [
        ('x0', 'milligrams of calcium'), 
        ('x1', 'milligrams of zinc'), 
        ('x2', 'milligrams of vitamin B1'), 
        ('x3', 'milligrams of vitamin B5')
    ], 
    'objective_function': '5.08*x0*x1 + 6.35*x1^2 + 6.26*x1*x2 + 6.59*x1*x3 + 3.56*x2^2', 
    'constraints': [
        '3.53*x0 <= 376',
        '15.02*x0 <= 184',
        '10.75*x1 <= 376',
        '13.63*x1 <= 184',
        '6.16*x2 <= 376',
        '8.91*x2 <= 184',
        '0.26*x3 <= 376',
        '3.1*x3 <= 184',
        '6.16*x2 + 0.26*x3 >= 81',
        '3.53^2*x0^2 + 10.75^2*x1^2 >= 58',
        '15.02*x0 + 13.63*x1 + 3.1*x3 >= 40',
        '15.02*x0 + 13.63*x1 + 8.91*x2 >= 40',
        '13.63*x1 + 8.91*x2 + 3.1*x3 >= 40',
        '15.02^2*x0^2 + 13.63^2*x1^2 + 3.1^2*x3^2 >= 24',
        '15.02*x0 + 13.63*x1 + 8.91*x2 >= 24',
        '13.63^2*x1^2 + 8.91^2*x2^2 + 3.1^2*x3^2 >= 24',
        '15.02*x0 + 13.63*x1 + 3.1*x3 >= 33',
        '15.02*x0 + 13.63*x1 + 8.91*x2 >= 33',
        '13.63*x1 + 8.91*x2 + 3.1*x3 >= 33',
        '3.53^2*x0^2 + 10.75^2*x1^2 <= 186',
        '10.75*x1 + 0.26*x3 <= 136',
        '3.53*x0 + 6.16*x2 <= 190',
        '3.53^2*x0^2 + 6.16^2*x2^2 + 0.26^2*x3^2 <= 370',
        '3.53*x0 + 10.75*x1 + 6.16*x2 + 0.26*x3 <= 370',
        '15.02^2*x0^2 + 3.1^2*x3^2 <= 159',
        '8.91*x2 + 3.1*x3 <= 142',
        '15.02^2*x0^2 + 8.91^2*x2^2 <= 50',
        '3.53^2*x0^2 + 10.75^2*x1^2 <= 147',
        '3.53^2*x0^2 + 10.75^2*x1^2 + 0.26^2*x3^2 <= 120',
        '3.53^2*x0^2 + 10.75^2*x1^2 + 6.16^2*x2^2 <= 102',
        '15.02^2*x0^2 + 8.91^2*x2^2 + 3.1^2*x3^2 <= 78',
        '15.02*x0 + 13.63*x1 + 3.1*x3 <= 78'
    ]
}
```

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

# Define the model
m = gp.Model()

# Define the variables
x0 = m.addVar(name="x0", lb=-gp.GRB.INFINITY)  # milligrams of calcium
x1 = m.addVar(name="x1", lb=-gp.GRB.INFINITY)  # milligrams of zinc
x2 = m.addVar(name="x2", lb=-gp.GRB.INFINITY)  # milligrams of vitamin B1
x3 = m.addVar(name="x3", lb=0, type=gp.GRB.INTEGER)  # milligrams of vitamin B5

# Define the objective function
m.setObjective(5.08*x0*x1 + 6.35*x1**2 + 6.26*x1*x2 + 6.59*x1*x3 + 3.56*x2**2, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(3.53*x0 <= 376)
m.addConstr(15.02*x0 <= 184)
m.addConstr(10.75*x1 <= 376)
m.addConstr(13.63*x1 <= 184)
m.addConstr(6.16*x2 <= 376)
m.addConstr(8.91*x2 <= 184)
m.addConstr(0.26*x3 <= 376)
m.addConstr(3.1*x3 <= 184)
m.addConstr(6.16*x2 + 0.26*x3 >= 81)
m.addConstr(3.53**2*x0**2 + 10.75**2*x1**2 >= 58)
m.addConstr(15.02*x0 + 13.63*x1 + 3.1*x3 >= 40)
m.addConstr(15.02*x0 + 13.63*x1 + 8.91*x2 >= 40)
m.addConstr(13.63*x1 + 8.91*x2 + 3.1*x3 >= 40)
m.addConstr(15.02**2*x0**2 + 13.63**2*x1**2 + 3.1**2*x3**2 >= 24)
m.addConstr(15.02*x0 + 13.63*x1 + 8.91*x2 >= 24)
m.addConstr(13.63**2*x1**2 + 8.91**2*x2**2 + 3.1**2*x3**2 >= 24)
m.addConstr(15.02*x0 + 13.63*x1 + 3.1*x3 >= 33)
m.addConstr(15.02*x0 + 13.63*x1 + 8.91*x2 >= 33)
m.addConstr(13.63*x1 + 8.91*x2 + 3.1*x3 >= 33)
m.addConstr(3.53**2*x0**2 + 10.75**2*x1**2 <= 186)
m.addConstr(10.75*x1 + 0.26*x3 <= 136)
m.addConstr(3.53*x0 + 6.16*x2 <= 190)
m.addConstr(3.53**2*x0**2 + 6.16**2*x2**2 + 0.26**2*x3**2 <= 370)
m.addConstr(3.53*x0 + 10.75*x1 + 6.16*x2 + 0.26*x3 <= 370)
m.addConstr(15.02**2*x0**2 + 3.1**2*x3**2 <= 159)
m.addConstr(8.91*x2 + 3.1*x3 <= 142)
m.addConstr(15.02**2*x0**2 + 8.91**2*x2**2 <= 50)
m.addConstr(3.53**2*x0**2 + 10.75**2*x1**2 <= 147)
m.addConstr(3.53**2*x0**2 + 10.75**2*x1**2 + 0.26**2*x3**2 <= 120)
m.addConstr(3.53**2*x0**2 + 10.75**2*x1**2 + 6.16**2*x2**2 <= 102)
m.addConstr(15.02**2*x0**2 + 8.91**2*x2**2 + 3.1**2*x3**2 <= 78)
m.addConstr(15.02*x0 + 13.63*x1 + 3.1*x3 <= 78)

# Solve the model
m.optimize()

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