## Step 1: Define the symbolic representation of the problem
The problem involves three variables: 'milligrams of vitamin K', 'milligrams of vitamin B2', and 'milligrams of vitamin E'. Let's denote these as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to minimize is $1 \times x_0 + 3 \times x_1 + 8 \times x_2$.

## 3: Define the constraints in symbolic notation
The constraints given are:
- $13.89x_1 + 6.47x_2 \geq 54$
- $5.94x_0 + 13.89x_1 \geq 27$
- $5.94x_0 + 13.89x_1 + 6.47x_2 \geq 55$
- $4.83x_0 + 7.06x_1 \geq 25$
- $4.83x_0 + 13.92x_2 \geq 30$
- $7.06x_1 + 13.92x_2 \geq 25$
- $4.83x_0 + 7.06x_1 + 13.92x_2 \geq 25$
- $2x_1 - 6x_2 \geq 0$
- $-3x_0 + 5x_1 \geq 0$
- $5.94x_0 + 6.47x_2 \leq 193$
- $7.06x_1 + 13.92x_2 \leq 84$
- $4.83x_0 + 13.92x_2 \leq 132$
- $x_0$ is an integer, $x_1$ and $x_2$ are continuous.

## 4: Create a symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x0', 'milligrams of vitamin K'), ('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of vitamin E')],
'objective_function': '1*x0 + 3*x1 + 8*x2',
'constraints': [
    '13.89*x1 + 6.47*x2 >= 54',
    '5.94*x0 + 13.89*x1 >= 27',
    '5.94*x0 + 13.89*x1 + 6.47*x2 >= 55',
    '4.83*x0 + 7.06*x1 >= 25',
    '4.83*x0 + 13.92*x2 >= 30',
    '7.06*x1 + 13.92*x2 >= 25',
    '4.83*x0 + 7.06*x1 + 13.92*x2 >= 25',
    '2*x1 - 6*x2 >= 0',
    '-3*x0 + 5*x1 >= 0',
    '5.94*x0 + 6.47*x2 <= 193',
    '7.06*x1 + 13.92*x2 <= 84',
    '4.83*x0 + 13.92*x2 <= 132'
]
}
```

## 5: Write the Gurobi code for the optimization problem
```python
import gurobi

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

    # Define the variables
    x0 = model.addVar(name='milligrams_of_vitamin_K', vtype=gurobi.GRB.INTEGER)
    x1 = model.addVar(name='milligrams_of_vitamin_B2')
    x2 = model.addVar(name='milligrams_of_vitamin_E')

    # Objective function
    model.setObjective(1*x0 + 3*x1 + 8*x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(13.89*x1 + 6.47*x2 >= 54)
    model.addConstr(5.94*x0 + 13.89*x1 >= 27)
    model.addConstr(5.94*x0 + 13.89*x1 + 6.47*x2 >= 55)
    model.addConstr(4.83*x0 + 7.06*x1 >= 25)
    model.addConstr(4.83*x0 + 13.92*x2 >= 30)
    model.addConstr(7.06*x1 + 13.92*x2 >= 25)
    model.addConstr(4.83*x0 + 7.06*x1 + 13.92*x2 >= 25)
    model.addConstr(2*x1 - 6*x2 >= 0)
    model.addConstr(-3*x0 + 5*x1 >= 0)
    model.addConstr(5.94*x0 + 6.47*x2 <= 193)
    model.addConstr(7.06*x1 + 13.92*x2 <= 84)
    model.addConstr(4.83*x0 + 13.92*x2 <= 132)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('x0: ', x0.varValue)
        print('x1: ', x1.varValue)
        print('x2: ', x2.varValue)
    else:
        print('The model is infeasible')

optimize_vitamins()
```