## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of vitamin B9', 'grams of protein', 'milligrams of vitamin B4', 'milligrams of vitamin B1']. Let's denote them as $x_1$, $x_2$, $x_3$, and $x_4$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_1$ for 'milligrams of vitamin B9'
- $x_2$ for 'grams of protein'
- $x_3$ for 'milligrams of vitamin B4'
- $x_4$ for 'milligrams of vitamin B1'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $7.04x_1 + 9.18x_2 + 9.99x_3 + 2.45x_4$.

## 4: List the constraints in symbolic notation
The constraints are:
1. $3x_1 \geq 3$ (but since $r0$ has $x0 = 3$ for $x_1$, this implies $x_1 = 3$ as a specific condition, not a range)
2. $9x_2 \geq 9$
3. $19x_3 \geq 19$
4. $3x_4 \geq 3$
5. $9x_2 + 19x_3 \geq 26$
6. $9x_2 + 3x_4 \geq 30$
7. $3x_1 + 9x_2 \geq 11$
8. $3x_1 + 19x_3 \geq 14$
9. $3x_1 + 9x_2 + 19x_3 + 3x_4 \geq 14$
10. $-9x_3 + 4x_4 \geq 0$
11. $3x_1 - 2x_3 \geq 0$
12. $6x_2 - 5x_3 \geq 0$
13. $3x_1 + 19x_3 \leq 51$
14. $19x_3 + 3x_4 \leq 66$
15. $3x_1 + 3x_4 \leq 71$
16. $x_1$ is an integer
17. $x_2$ is a real number
18. $x_3$ is an integer
19. $x_4$ is a real number

## 5: Create the symbolic representation dictionary
```json
{
    'sym_variables': [
        ('x1', 'milligrams of vitamin B9'), 
        ('x2', 'grams of protein'), 
        ('x3', 'milligrams of vitamin B4'), 
        ('x4', 'milligrams of vitamin B1')
    ], 
    'objective_function': '7.04*x1 + 9.18*x2 + 9.99*x3 + 2.45*x4', 
    'constraints': [
        'x1 = 3',
        '9*x2 >= 9',
        '19*x3 >= 19',
        '3*x4 >= 3',
        '9*x2 + 19*x3 >= 26',
        '9*x2 + 3*x4 >= 30',
        '3*x1 + 9*x2 >= 11',
        '3*x1 + 19*x3 >= 14',
        '3*x1 + 9*x2 + 19*x3 + 3*x4 >= 14',
        '-9*x3 + 4*x4 >= 0',
        '3*x1 - 2*x3 >= 0',
        '6*x2 - 5*x3 >= 0',
        '3*x1 + 19*x3 <= 51',
        '19*x3 + 3*x4 <= 66',
        '3*x1 + 3*x4 <= 71'
    ]
}
```

## 6: Write the Gurobi code
```python
import gurobi

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

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

    # Objective function
    model.setObjective(7.04 * x1 + 9.18 * x2 + 9.99 * x3 + 2.45 * x4, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(x1 == 3)
    model.addConstr(9 * x2 >= 9)
    model.addConstr(19 * x3 >= 19)
    model.addConstr(3 * x4 >= 3)
    model.addConstr(9 * x2 + 19 * x3 >= 26)
    model.addConstr(9 * x2 + 3 * x4 >= 30)
    model.addConstr(3 * x1 + 9 * x2 >= 11)
    model.addConstr(3 * x1 + 19 * x3 >= 14)
    model.addConstr(3 * x1 + 9 * x2 + 19 * x3 + 3 * x4 >= 14)
    model.addConstr(-9 * x3 + 4 * x4 >= 0)
    model.addConstr(3 * x1 - 2 * x3 >= 0)
    model.addConstr(6 * x2 - 5 * x3 >= 0)
    model.addConstr(3 * x1 + 19 * x3 <= 51)
    model.addConstr(19 * x3 + 3 * x4 <= 66)
    model.addConstr(3 * x1 + 3 * x4 <= 71)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print('Objective: ', model.objval)
        print('x1: ', x1.varValue)
        print('x2: ', x2.varValue)
        print('x3: ', x3.varValue)
        print('x4: ', x4.varValue)
    else:
        print('The model is infeasible')

optimize_problem()
```