## Step 1: Define the symbolic representation of the problem
The problem involves maximizing an objective function subject to several constraints. The variables are:
- $x_0$ for milligrams of zinc
- $x_1$ for milligrams of calcium
- $x_2$ for milligrams of vitamin B2
- $x_3$ for milligrams of vitamin K

The objective function to maximize is: $5x_0 + 3x_1 + 5x_2 + 7x_3$

## Step 2: List all constraints in symbolic notation
Constraints:
1. $7x_0 \leq 93$
2. $8x_0 \leq 63$
3. $9x_1 \leq 93$
4. $1x_1 \leq 63$
5. $7x_2 \leq 93$
6. $10x_2 \leq 63$
7. $11x_3 \leq 93$
8. $5x_3 \leq 63$
9. $7x_0 + 7x_2 + 11x_3 \geq 19$
10. $7x_0 + 9x_1 + 11x_3 \geq 19$
11. $7x_0 + 9x_1 + 7x_2 \geq 19$
12. $7x_0 + 7x_2 + 11x_3 \geq 15$
13. $7x_0 + 9x_1 + 11x_3 \geq 15$
14. $7x_0 + 9x_1 + 7x_2 \geq 15$
15. $7x_0 + 7x_2 + 11x_3 \geq 14$
16. $7x_0 + 9x_1 + 11x_3 \geq 14$
17. $7x_0 + 9x_1 + 7x_2 \geq 14$
18. $8x_0 + 1x_1 + 10x_2 \geq 10$
19. $8x_0 + 10x_2 + 5x_3 \geq 10$
20. $8x_0 + 1x_1 + 10x_2 \geq 10$
21. $8x_0 + 10x_2 + 5x_3 \geq 10$
22. $7x_0 + 11x_3 \leq 77$
23. $7x_0 + 9x_1 \leq 54$
24. $7x_0 + 7x_2 \leq 60$
25. $7x_0 + 7x_2 + 11x_3 \leq 88$
26. $7x_0 + 9x_1 + 7x_2 \leq 39$
27. $7x_0 + 9x_1 + 7x_2 + 11x_3 \leq 39$
28. $8x_0 + 1x_1 \leq 48$
29. $10x_2 + 5x_3 \leq 53$
30. $8x_0 + 1x_1 + 10x_2 + 5x_3 \leq 53$

## Step 3: Define the symbolic representation
```json
{
    'sym_variables': [
        ('x0', 'milligrams of zinc'),
        ('x1', 'milligrams of calcium'),
        ('x2', 'milligrams of vitamin B2'),
        ('x3', 'milligrams of vitamin K')
    ],
    'objective_function': '5*x0 + 3*x1 + 5*x2 + 7*x3',
    'constraints': [
        '7*x0 <= 93',
        '8*x0 <= 63',
        '9*x1 <= 93',
        '1*x1 <= 63',
        '7*x2 <= 93',
        '10*x2 <= 63',
        '11*x3 <= 93',
        '5*x3 <= 63',
        '7*x0 + 7*x2 + 11*x3 >= 19',
        '7*x0 + 9*x1 + 11*x3 >= 19',
        '7*x0 + 9*x1 + 7*x2 >= 19',
        '7*x0 + 7*x2 + 11*x3 >= 15',
        '7*x0 + 9*x1 + 11*x3 >= 15',
        '7*x0 + 9*x1 + 7*x2 >= 15',
        '7*x0 + 7*x2 + 11*x3 >= 14',
        '7*x0 + 9*x1 + 11*x3 >= 14',
        '7*x0 + 9*x1 + 7*x2 >= 14',
        '8*x0 + 1*x1 + 10*x2 >= 10',
        '8*x0 + 10*x2 + 5*x3 >= 10',
        '8*x0 + 1*x1 + 10*x2 >= 10',
        '8*x0 + 10*x2 + 5*x3 >= 10',
        '7*x0 + 11*x3 <= 77',
        '7*x0 + 9*x1 <= 54',
        '7*x0 + 7*x2 <= 60',
        '7*x0 + 7*x2 + 11*x3 <= 88',
        '7*x0 + 9*x1 + 7*x2 <= 39',
        '7*x0 + 9*x1 + 7*x2 + 11*x3 <= 39',
        '8*x0 + 1*x1 <= 48',
        '10*x2 + 5*x3 <= 53',
        '8*x0 + 1*x1 + 10*x2 + 5*x3 <= 53',
        'x0 % 1 == 0',
        'x2 % 1 == 0',
        'x3 % 1 == 0'
    ]
}
```

## Step 4: Implement the optimization problem using Gurobi
```python
import gurobi as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define the variables
x0 = m.addVar(name="x0", lb=0, Integrality=1)  # milligrams of zinc
x1 = m.addVar(name="x1", lb=0)  # milligrams of calcium
x2 = m.addVar(name="x2", lb=0, Integrality=1)  # milligrams of vitamin B2
x3 = m.addVar(name="x3", lb=0, Integrality=1)  # milligrams of vitamin K

# Objective function
m.setObjective(5 * x0 + 3 * x1 + 5 * x2 + 7 * x3, gp.GRB.MAXIMIZE)

# Constraints
m.addConstr(7 * x0 <= 93)
m.addConstr(8 * x0 <= 63)
m.addConstr(9 * x1 <= 93)
m.addConstr(1 * x1 <= 63)
m.addConstr(7 * x2 <= 93)
m.addConstr(10 * x2 <= 63)
m.addConstr(11 * x3 <= 93)
m.addConstr(5 * x3 <= 63)
m.addConstr(7 * x0 + 7 * x2 + 11 * x3 >= 19)
m.addConstr(7 * x0 + 9 * x1 + 11 * x3 >= 19)
m.addConstr(7 * x0 + 9 * x1 + 7 * x2 >= 19)
m.addConstr(7 * x0 + 7 * x2 + 11 * x3 >= 15)
m.addConstr(7 * x0 + 9 * x1 + 11 * x3 >= 15)
m.addConstr(7 * x0 + 9 * x1 + 7 * x2 >= 15)
m.addConstr(7 * x0 + 7 * x2 + 11 * x3 >= 14)
m.addConstr(7 * x0 + 9 * x1 + 11 * x3 >= 14)
m.addConstr(7 * x0 + 9 * x1 + 7 * x2 >= 14)
m.addConstr(8 * x0 + 1 * x1 + 10 * x2 >= 10)
m.addConstr(8 * x0 + 10 * x2 + 5 * x3 >= 10)
m.addConstr(8 * x0 + 1 * x1 + 10 * x2 >= 10)
m.addConstr(8 * x0 + 10 * x2 + 5 * x3 >= 10)
m.addConstr(7 * x0 + 11 * x3 <= 77)
m.addConstr(7 * x0 + 9 * x1 <= 54)
m.addConstr(7 * x0 + 7 * x2 <= 60)
m.addConstr(7 * x0 + 7 * x2 + 11 * x3 <= 88)
m.addConstr(7 * x0 + 9 * x1 + 7 * x2 <= 39)
m.addConstr(7 * x0 + 9 * x1 + 7 * x2 + 11 * x3 <= 39)
m.addConstr(8 * x0 + 1 * x1 <= 48)
m.addConstr(10 * x2 + 5 * x3 <= 53)
m.addConstr(8 * x0 + 1 * x1 + 10 * x2 + 5 * x3 <= 53)

# Optimize 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")
```