## Step 1: Define the symbolic representation of the variables
The variables are defined as follows: 
- $x_1$ represents 'grams of carbohydrates'
- $x_2$ represents 'milligrams of vitamin A'
- $x_3$ represents 'milligrams of vitamin B3'
- $x_4$ represents 'milligrams of potassium'
- $x_5$ represents 'milligrams of magnesium'

## Step 2: Formulate the objective function
The objective function to minimize is $8.89x_1 + 1.58x_2 + 5.34x_3 + 4.52x_4 + 6.46x_5$.

## 3: Define the constraints
The constraints are:
- $24x_1 \leq 482$
- $10x_2 \leq 482$
- $7x_3 \leq 482$
- $18x_4 \leq 482$
- $6x_5 \leq 482$
- $24x_1 + 18x_4 \geq 96$
- $7x_3 + 18x_4 \geq 83$
- $24x_1 + 10x_2 \geq 80$
- $18x_4 + 6x_5 \geq 96$
- $7x_3 + 18x_4 + 6x_5 \geq 94$
- $24x_1 + 10x_2 + 6x_5 \geq 94$
- $10x_2 + 18x_4 + 6x_5 \geq 94$
- $7x_3 + 18x_4 + 6x_5 \geq 78$
- $24x_1 + 10x_2 + 6x_5 \geq 78$
- $10x_2 + 18x_4 + 6x_5 \geq 78$
- $7x_3 + 18x_4 + 6x_5 \geq 59$
- $24x_1 + 10x_2 + 6x_5 \geq 59$
- $10x_2 + 18x_4 + 6x_5 \geq 59$
- $24x_1 + 10x_2 + 7x_3 + 18x_4 + 6x_5 \geq 59$
- $5x_3 - 3x_5 \geq 0$
- $8x_1 - 5x_5 \geq 0$
- $24x_1 + 18x_4 \leq 179$
- $10x_2 + 7x_3 \leq 133$
- $7x_3 + 18x_4 \leq 409$
- $24x_1 + 10x_2 + 6x_5 \leq 461$

## 4: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x1', 'grams of carbohydrates'), 
        ('x2', 'milligrams of vitamin A'), 
        ('x3', 'milligrams of vitamin B3'), 
        ('x4', 'milligrams of potassium'), 
        ('x5', 'milligrams of magnesium')
    ], 
    'objective_function': '8.89*x1 + 1.58*x2 + 5.34*x3 + 4.52*x4 + 6.46*x5', 
    'constraints': [
        '24*x1 <= 482',
        '10*x2 <= 482',
        '7*x3 <= 482',
        '18*x4 <= 482',
        '6*x5 <= 482',
        '24*x1 + 18*x4 >= 96',
        '7*x3 + 18*x4 >= 83',
        '24*x1 + 10*x2 >= 80',
        '18*x4 + 6*x5 >= 96',
        '7*x3 + 18*x4 + 6*x5 >= 94',
        '24*x1 + 10*x2 + 6*x5 >= 94',
        '10*x2 + 18*x4 + 6*x5 >= 94',
        '7*x3 + 18*x4 + 6*x5 >= 78',
        '24*x1 + 10*x2 + 6*x5 >= 78',
        '10*x2 + 18*x4 + 6*x5 >= 78',
        '7*x3 + 18*x4 + 6*x5 >= 59',
        '24*x1 + 10*x2 + 6*x5 >= 59',
        '10*x2 + 18*x4 + 6*x5 >= 59',
        '24*x1 + 10*x2 + 7*x3 + 18*x4 + 6*x5 >= 59',
        '5*x3 - 3*x5 >= 0',
        '8*x1 - 5*x5 >= 0',
        '24*x1 + 18*x4 <= 179',
        '10*x2 + 7*x3 <= 133',
        '7*x3 + 18*x4 <= 409',
        '24*x1 + 10*x2 + 6*x5 <= 461'
    ]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="x1", lb=0)  # grams of carbohydrates
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin A
    x3 = model.addVar(name="x3", lb=0)  # milligrams of vitamin B3
    x4 = model.addVar(name="x4", lb=0)  # milligrams of potassium
    x5 = model.addVar(name="x5", lb=0)  # milligrams of magnesium

    # Objective function
    model.setObjective(8.89 * x1 + 1.58 * x2 + 5.34 * x3 + 4.52 * x4 + 6.46 * x5, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(24 * x1 <= 482)
    model.addConstr(10 * x2 <= 482)
    model.addConstr(7 * x3 <= 482)
    model.addConstr(18 * x4 <= 482)
    model.addConstr(6 * x5 <= 482)

    model.addConstr(24 * x1 + 18 * x4 >= 96)
    model.addConstr(7 * x3 + 18 * x4 >= 83)
    model.addConstr(24 * x1 + 10 * x2 >= 80)
    model.addConstr(18 * x4 + 6 * x5 >= 96)
    model.addConstr(7 * x3 + 18 * x4 + 6 * x5 >= 94)
    model.addConstr(24 * x1 + 10 * x2 + 6 * x5 >= 94)
    model.addConstr(10 * x2 + 18 * x4 + 6 * x5 >= 94)
    model.addConstr(7 * x3 + 18 * x4 + 6 * x5 >= 78)
    model.addConstr(24 * x1 + 10 * x2 + 6 * x5 >= 78)
    model.addConstr(10 * x2 + 18 * x4 + 6 * x5 >= 78)
    model.addConstr(7 * x3 + 18 * x4 + 6 * x5 >= 59)
    model.addConstr(24 * x1 + 10 * x2 + 6 * x5 >= 59)
    model.addConstr(10 * x2 + 18 * x4 + 6 * x5 >= 59)
    model.addConstr(24 * x1 + 10 * x2 + 7 * x3 + 18 * x4 + 6 * x5 >= 59)

    model.addConstr(5 * x3 - 3 * x5 >= 0)
    model.addConstr(8 * x1 - 5 * x5 >= 0)

    model.addConstr(24 * x1 + 18 * x4 <= 179)
    model.addConstr(10 * x2 + 7 * x3 <= 133)
    model.addConstr(7 * x3 + 18 * x4 <= 409)
    model.addConstr(24 * x1 + 10 * x2 + 6 * x5 <= 461)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"x1: {x1.varValue}")
        print(f"x2: {x2.varValue}")
        print(f"x3: {x3.varValue}")
        print(f"x4: {x4.varValue}")
        print(f"x5: {x5.varValue}")
        print(f"Objective: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize_problem()
```