## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B12', 'milligrams of vitamin B2', and 'milligrams of vitamin D'. Let's denote them as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language objects into symbolic notation
The symbolic representation of the variables is:
- $x_0$ : milligrams of vitamin B12
- $x_1$ : milligrams of vitamin B2
- $x_2$ : milligrams of vitamin D

## 3: Define the objective function in symbolic notation
The objective function to minimize is $1x_0 + 1x_1 + 3x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $6x_0 + 4x_1 \geq 30$
- $6x_0 + 5x_2 \geq 19$
- $6x_0 + 4x_1 + 5x_2 \geq 21$
- $6x_0 + 4x_1 + 5x_2 \geq 21$ (same as the previous one, so we keep it as is)
- $13x_1 + 4x_2 \geq 16$
- $10x_0 + 13x_1 \geq 39$
- $10x_0 + 13x_1 + 4x_2 \geq 23$
- $10x_0 + 13x_1 + 4x_2 \geq 23$ (same as the previous one, so we keep it as is)
- $2x_0 - 7x_2 \geq 0$
- $-7x_1 + 5x_2 \geq 0$
- $-x_0 + 7x_1 \geq 0$
- $4x_1 + 5x_2 \leq 78$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B12'),
        ('x1', 'milligrams of vitamin B2'),
        ('x2', 'milligrams of vitamin D')
    ],
    'objective_function': '1*x0 + 1*x1 + 3*x2',
    'constraints': [
        '6*x0 + 4*x1 >= 30',
        '6*x0 + 5*x2 >= 19',
        '6*x0 + 4*x1 + 5*x2 >= 21',
        '13*x1 + 4*x2 >= 16',
        '10*x0 + 13*x1 >= 39',
        '10*x0 + 13*x1 + 4*x2 >= 23',
        '2*x0 - 7*x2 >= 0',
        '-7*x1 + 5*x2 >= 0',
        '-x0 + 7*x1 >= 0',
        '4*x1 + 5*x2 <= 78'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B12
    x1 = model.addVar(name="x1", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin B2
    x2 = model.addVar(name="x2", lb=-gurobi.GRB.INFINITY)  # milligrams of vitamin D

    # Define the objective function
    model.setObjective(x0 + x1 + 3 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(6 * x0 + 4 * x1 >= 30)
    model.addConstr(6 * x0 + 5 * x2 >= 19)
    model.addConstr(6 * x0 + 4 * x1 + 5 * x2 >= 21)
    model.addConstr(13 * x1 + 4 * x2 >= 16)
    model.addConstr(10 * x0 + 13 * x1 >= 39)
    model.addConstr(10 * x0 + 13 * x1 + 4 * x2 >= 23)
    model.addConstr(2 * x0 - 7 * x2 >= 0)
    model.addConstr(-7 * x1 + 5 * x2 >= 0)
    model.addConstr(-x0 + 7 * x1 >= 0)
    model.addConstr(4 * x1 + 5 * x2 <= 78)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B12: {x0.varValue}")
        print(f"Milligrams of vitamin B2: {x1.varValue}")
        print(f"Milligrams of vitamin D: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```