## Step 1: Define the symbolic representation of the variables
The variables are defined as follows:
- $x_1$ represents 'milligrams of vitamin B4'
- $x_2$ represents 'milligrams of zinc'
- $x_3$ represents 'milligrams of iron'
- $x_4$ represents 'milligrams of calcium'
- $x_5$ represents 'milligrams of magnesium'

## Step 2: Translate the objective function into algebraic terms
The objective function to maximize is $9x_1 + 6x_2 + 3x_3 + 2x_4 + x_5$.

## 3: List the constraints in algebraic terms
The constraints are:
1. $11x_1 \geq 11$ (but since $x_1$ must be an integer, and this seems to be an index, we'll consider it as $x_1 \geq 0$ and use the index for calculation directly in the model)
2. $4x_2$ 
3. $13x_3$ 
4. $2x_4$ 
5. $9x_5$ 
6. $11x_1 + 4x_2 \geq 30$
7. $4x_2 + 13x_3 \geq 29$
8. $11x_1 + 2x_4 \geq 24$
9. $13x_3 + 2x_4 \geq 24$
10. $4x_2 + 9x_5 \geq 30$
11. $4x_2 + 2x_4 \geq 27$
12. $4x_2 + 13x_3 \leq 184$
13. $4x_2 + 2x_4 \leq 62$
14. $13x_3 + 9x_5 \leq 146$
15. $11x_1 + 4x_2 \leq 55$
16. $11x_1 + 13x_3 + 2x_4 \leq 174$
17. $11x_1 + 4x_2 + 13x_3 \leq 162$
18. $11x_1 + 2x_4 + 9x_5 \leq 59$
19. $11x_1 + 4x_2 + 13x_3 + 2x_4 + 9x_5 \leq 59$
20. $x_1$ is an integer.

## 4: Create the Gurobi model
```python
import gurobi

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

# Define the variables
x1 = m.addVar(name='x1', vtype='I') # integer
x2 = m.addVar(name='x2') 
x3 = m.addVar(name='x3') 
x4 = m.addVar(name='x4') 
x5 = m.addVar(name='x5') 

# Objective function
m.setObjective(9*x1 + 6*x2 + 3*x3 + 2*x4 + x5, gurobi.GRB.MAXIMIZE)

# Constraints
m.addConstr(11*x1 + 4*x2 >= 30)
m.addConstr(4*x2 + 13*x3 >= 29)
m.addConstr(11*x1 + 2*x4 >= 24)
m.addConstr(13*x3 + 2*x4 >= 24)
m.addConstr(4*x2 + 9*x5 >= 30)
m.addConstr(4*x2 + 2*x4 >= 27)
m.addConstr(4*x2 + 13*x3 <= 184)
m.addConstr(4*x2 + 2*x4 <= 62)
m.addConstr(13*x3 + 9*x5 <= 146)
m.addConstr(11*x1 + 4*x2 <= 55)
m.addConstr(11*x1 + 13*x3 + 2*x4 <= 174)
m.addConstr(11*x1 + 4*x2 + 13*x3 <= 162)
m.addConstr(11*x1 + 2*x4 + 9*x5 <= 59)
m.addConstr(11*x1 + 4*x2 + 13*x3 + 2*x4 + 9*x5 <= 59)

# Solve the model
m.optimize()

# Print the solution
if m.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', m.objVal)
    print('x1: ', x1.varValue)
    print('x2: ', x2.varValue)
    print('x3: ', x3.varValue)
    print('x4: ', x4.varValue)
    print('x5: ', x5.varValue)
else:
    print('No solution found')
```

## 5: Symbolic Representation
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B4'),
        ('x2', 'milligrams of zinc'),
        ('x3', 'milligrams of iron'),
        ('x4', 'milligrams of calcium'),
        ('x5', 'milligrams of magnesium')
    ],
    'objective_function': '9*x1 + 6*x2 + 3*x3 + 2*x4 + x5',
    'constraints': [
        '11*x1 + 4*x2 >= 30',
        '4*x2 + 13*x3 >= 29',
        '11*x1 + 2*x4 >= 24',
        '13*x3 + 2*x4 >= 24',
        '4*x2 + 9*x5 >= 30',
        '4*x2 + 2*x4 >= 27',
        '4*x2 + 13*x3 <= 184',
        '4*x2 + 2*x4 <= 62',
        '13*x3 + 9*x5 <= 146',
        '11*x1 + 4*x2 <= 55',
        '11*x1 + 13*x3 + 2*x4 <= 174',
        '11*x1 + 4*x2 + 13*x3 <= 162',
        '11*x1 + 2*x4 + 9*x5 <= 59',
        '11*x1 + 4*x2 + 13*x3 + 2*x4 + 9*x5 <= 59'
    ]
}
```