## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of zinc', 'milligrams of iron', 'milligrams of potassium', and 'milligrams of vitamin B5'. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Define the symbolic representation of the problem
The objective function to minimize is $7x_1 + 3x_2 + 5x_3 + x_4$.

## Step 3: List the constraints
The constraints based on the given information are:
- $10.05x_1 \geq 0$ (implicit, as $x_1$ is non-negative)
- $0.29x_1 \geq 0$ (implicit, as $x_1$ is non-negative)
- $9.7x_2 \geq 0$ (implicit, as $x_2$ is non-negative)
- $7.56x_2 \geq 0$ (implicit, as $x_2$ is non-negative)
- $3.38x_3 \geq 0$ (implicit, as $x_3$ is non-negative)
- $0.03x_3 \geq 0$ (implicit, as $x_3$ is non-negative)
- $10.59x_4 \geq 0$ (implicit, as $x_4$ is non-negative)
- $9.46x_4 \geq 0$ (implicit, as $x_4$ is non-negative)
- $10.05x_1 + 10.59x_4 \geq 33$
- $9.7x_2 + 10.59x_4 \geq 54$
- $9.7x_2 + 3.38x_3 \geq 40$
- $10.05x_1 + 9.7x_2 \geq 35$
- $10.05x_1 + 9.7x_2 + 3.38x_3 + 10.59x_4 \geq 35$
- $0.29x_1 + 7.56x_2 \geq 44$
- $7.56x_2 + 0.03x_3 \geq 25$
- $0.03x_3 + 9.46x_4 \geq 19$
- $0.29x_1 + 7.56x_2 + 0.03x_3 + 9.46x_4 \geq 19$
- $-9x_2 + 2x_4 \geq 0$
- $9x_3 - 8x_4 \geq 0$

## 4: Convert the problem into Gurobi code
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()

    # Define variables
    x1 = model.addVar(name="milligrams_of_zinc", lb=0)
    x2 = model.addVar(name="milligrams_of_iron", lb=0)
    x3 = model.addVar(name="milligrams_of_potassium", lb=0)
    x4 = model.addVar(name="milligrams_of_vitamin_B5", lb=0)

    # Objective function
    model.setObjective(7 * x1 + 3 * x2 + 5 * x3 + x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(10.05 * x1 + 10.59 * x4 >= 33)
    model.addConstr(9.7 * x2 + 10.59 * x4 >= 54)
    model.addConstr(9.7 * x2 + 3.38 * x3 >= 40)
    model.addConstr(10.05 * x1 + 9.7 * x2 >= 35)
    model.addConstr(10.05 * x1 + 9.7 * x2 + 3.38 * x3 + 10.59 * x4 >= 35)
    model.addConstr(0.29 * x1 + 7.56 * x2 >= 44)
    model.addConstr(7.56 * x2 + 0.03 * x3 >= 25)
    model.addConstr(0.03 * x3 + 9.46 * x4 >= 19)
    model.addConstr(0.29 * x1 + 7.56 * x2 + 0.03 * x3 + 9.46 * x4 >= 19)
    model.addConstr(-9 * x2 + 2 * x4 >= 0)
    model.addConstr(9 * x3 - 8 * x4 >= 0)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of zinc: {x1.varValue}")
        print(f"Milligrams of iron: {x2.varValue}")
        print(f"Milligrams of potassium: {x3.varValue}")
        print(f"Milligrams of vitamin B5: {x4.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```

## 5: Symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'milligrams of zinc'), 
        ('x2', 'milligrams of iron'), 
        ('x3', 'milligrams of potassium'), 
        ('x4', 'milligrams of vitamin B5')
    ], 
    'objective_function': '7*x1 + 3*x2 + 5*x3 + x4', 
    'constraints': [
        '10.05*x1 + 10.59*x4 >= 33',
        '9.7*x2 + 10.59*x4 >= 54',
        '9.7*x2 + 3.38*x3 >= 40',
        '10.05*x1 + 9.7*x2 >= 35',
        '10.05*x1 + 9.7*x2 + 3.38*x3 + 10.59*x4 >= 35',
        '0.29*x1 + 7.56*x2 >= 44',
        '7.56*x2 + 0.03*x3 >= 25',
        '0.03*x3 + 9.46*x4 >= 19',
        '0.29*x1 + 7.56*x2 + 0.03*x3 + 9.46*x4 >= 19',
        '-9*x2 + 2*x4 >= 0',
        '9*x3 - 8*x4 >= 0'
    ]
}
```