## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_0$ represents 'milligrams of vitamin C'
- $x_1$ represents 'milligrams of zinc'
- $x_2$ represents 'milligrams of magnesium'
- $x_3$ represents 'milligrams of vitamin E'

## Step 2: Translate the objective function into algebraic terms
The objective function to maximize is: $4x_0 + 4x_1 + x_2 + 9x_3$

## Step 3: List all the constraints in algebraic terms
Constraints:
1. $14x_1 + 16x_2 \geq 34$
2. $18x_0 + 16x_2 \geq 20$
3. $18x_0 + 16x_2 + 19x_3 \geq 41$
4. $16x_0 + 18x_1 + x_2 \geq 34$
5. $16x_0 + 18x_1 + 14x_3 \geq 34$
6. $16x_0 + 18x_1 + x_2 \geq 41$
7. $16x_0 + 18x_1 + 14x_3 \geq 41$
8. $18x_0 + 18x_3 \geq 32$
9. $18x_0 + 18x_2 \geq 23$
10. $19x_1 + 18x_2 \geq 42$
11. $18x_0 + 19x_1 \geq 21$
12. $14x_1 + 16x_2 \leq 74$
13. $16x_2 + 19x_3 \leq 108$
14. $14x_1 + 19x_3 \leq 71$
15. $18x_0 + 19x_3 \leq 172$
16. $18x_0 + 14x_1 + 16x_2 + 19x_3 \leq 172$
17. $18x_1 + 14x_3 \leq 75$
18. $19x_1 + x_2 \leq 130$
19. $16x_0 + x_2 \leq 98$
20. $x_2 + 14x_3 \leq 174$
21. $16x_0 + 18x_1 + 14x_3 \leq 118$
22. $16x_0 + x_2 + 14x_3 \leq 63$
23. $16x_0 + 18x_1 + x_2 + 14x_3 \leq 63$
24. $19x_1 + 18x_2 \leq 162$
25. $18x_0 + 18x_3 \leq 197$
26. $18x_0 + 18x_2 \leq 147$
27. $19x_1 + 18x_3 \leq 134$
28. $18x_0 + 19x_1 \leq 75$
29. $18x_2 + 18x_3 \leq 111$
30. $18x_0 + 19x_1 + 18x_3 \leq 81$
31. $18x_0 + 19x_1 + 18x_2 + 18x_3 \leq 81$

## 4: Create the Gurobi model and variables
We will now create a Gurobi model and define the variables.

```python
import gurobi

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

# Define the variables
x0 = model.addVar(name="x0", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin C
x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of zinc
x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of magnesium
x3 = model.addVar(name="x3", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin E

# Define the objective function
model.setObjective(4 * x0 + 4 * x1 + x2 + 9 * x3, gurobi.GRB.MAXIMIZE)

# Add constraints
# Constraints
model.addConstr(14 * x1 + 16 * x2 >= 34)
model.addConstr(18 * x0 + 16 * x2 >= 20)
model.addConstr(18 * x0 + 16 * x2 + 19 * x3 >= 41)
model.addConstr(16 * x0 + 18 * x1 + x2 >= 34)
model.addConstr(16 * x0 + 18 * x1 + 14 * x3 >= 34)
model.addConstr(16 * x0 + 18 * x1 + x2 >= 41)
model.addConstr(16 * x0 + 18 * x1 + 14 * x3 >= 41)
model.addConstr(18 * x0 + 18 * x3 >= 32)
model.addConstr(18 * x0 + 18 * x2 >= 23)
model.addConstr(19 * x1 + 18 * x2 >= 42)
model.addConstr(18 * x0 + 19 * x1 >= 21)
model.addConstr(14 * x1 + 16 * x2 <= 74)
model.addConstr(16 * x2 + 19 * x3 <= 108)
model.addConstr(14 * x1 + 19 * x3 <= 71)
model.addConstr(18 * x0 + 19 * x3 <= 172)
model.addConstr(18 * x0 + 14 * x1 + 16 * x2 + 19 * x3 <= 172)
model.addConstr(18 * x1 + 14 * x3 <= 75)
model.addConstr(19 * x1 + x2 <= 130)
model.addConstr(16 * x0 + x2 <= 98)
model.addConstr(x2 + 14 * x3 <= 174)
model.addConstr(16 * x0 + 18 * x1 + 14 * x3 <= 118)
model.addConstr(16 * x0 + x2 + 14 * x3 <= 63)
model.addConstr(16 * x0 + 18 * x1 + x2 + 14 * x3 <= 63)
model.addConstr(19 * x1 + 18 * x2 <= 162)
model.addConstr(18 * x0 + 18 * x3 <= 197)
model.addConstr(18 * x0 + 18 * x2 <= 147)
model.addConstr(19 * x1 + 18 * x3 <= 134)
model.addConstr(18 * x0 + 19 * x1 <= 75)
model.addConstr(18 * x2 + 18 * x3 <= 111)
model.addConstr(18 * x0 + 19 * x1 + 18 * x3 <= 81)
model.addConstr(18 * x0 + 19 * x1 + 18 * x2 + 18 * x3 <= 81)

# Solve the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print("Optimal solution found.")
    print("milligrams of vitamin C:", x0.varValue)
    print("milligrams of zinc:", x1.varValue)
    print("milligrams of magnesium:", x2.varValue)
    print("milligrams of vitamin E:", x3.varValue)
    print("Objective function value:", model.objVal)
else:
    print("No optimal solution found.")
```

## 5: Symbolic representation
The symbolic representation is as follows:
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin C'),
        ('x1', 'milligrams of zinc'),
        ('x2', 'milligrams of magnesium'),
        ('x3', 'milligrams of vitamin E')
    ],
    'objective_function': '4*x0 + 4*x1 + x2 + 9*x3',
    'constraints': [
        '14*x1 + 16*x2 >= 34',
        '18*x0 + 16*x2 >= 20',
        '18*x0 + 16*x2 + 19*x3 >= 41',
        '16*x0 + 18*x1 + x2 >= 34',
        '16*x0 + 18*x1 + 14*x3 >= 34',
        '16*x0 + 18*x1 + x2 >= 41',
        '16*x0 + 18*x1 + 14*x3 >= 41',
        '18*x0 + 18*x3 >= 32',
        '18*x0 + 18*x2 >= 23',
        '19*x1 + 18*x2 >= 42',
        '18*x0 + 19*x1 >= 21',
        '14*x1 + 16*x2 <= 74',
        '16*x2 + 19*x3 <= 108',
        '14*x1 + 19*x3 <= 71',
        '18*x0 + 19*x3 <= 172',
        '18*x0 + 14*x1 + 16*x2 + 19*x3 <= 172',
        '18*x1 + 14*x3 <= 75',
        '19*x1 + x2 <= 130',
        '16*x0 + x2 <= 98',
        'x2 + 14*x3 <= 174',
        '16*x0 + 18*x1 + 14*x3 <= 118',
        '16*x0 + x2 + 14*x3 <= 63',
        '16*x0 + 18*x1 + x2 + 14*x3 <= 63',
        '19*x1 + 18*x2 <= 162',
        '18*x0 + 18*x3 <= 197',
        '18*x0 + 18*x2 <= 147',
        '19*x1 + 18*x3 <= 134',
        '18*x0 + 19*x1 <= 75',
        '18*x2 + 18*x3 <= 111',
        '18*x0 + 19*x1 + 18*x3 <= 81',
        '18*x0 + 19*x1 + 18*x2 + 18*x3 <= 81'
    ]
}
```