## Step 1: Define the symbolic representation of the variables
The variables are 'milligrams of vitamin B9', 'milligrams of magnesium', and 'milligrams of calcium', which can be represented symbolically 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:
- $x_0$ for 'milligrams of vitamin B9'
- $x_1$ for 'milligrams of magnesium'
- $x_2$ for 'milligrams of calcium'

## 3: Define the objective function in symbolic notation
The objective function to minimize is $8x_0 + 7x_1 + 7x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $9x_0 \leq 284$
- $5x_0 \leq 392$
- $13x_1 \leq 284$
- $23x_1 \leq 392$
- $12x_2 \leq 284$
- $17x_2 \leq 392$
- $9x_0 + 12x_2 \geq 35$
- $9x_0 + 13x_1 + 12x_2 \geq 56$
- $9x_0 + 13x_1 + 12x_2 \geq 56$ (same as the previous one, so we keep it as is)
- $5x_0 + 17x_2 \geq 112$
- $23x_1 + 17x_2 \geq 79$
- $5x_0 + 23x_1 \geq 112$
- $5x_0 + 23x_1 + 17x_2 \geq 112$
- $-3x_1 + 8x_2 \geq 0$
- $-3x_0 + 10x_2 \geq 0$
- $13x_1 + 12x_2 \leq 213$
- $9x_0 + 12x_2 \leq 104$
- $9x_0 + 13x_1 + 12x_2 \leq 255$

## 5: Provide the symbolic representation of the problem
```json
{
    'sym_variables': [
        ('x0', 'milligrams of vitamin B9'),
        ('x1', 'milligrams of magnesium'),
        ('x2', 'milligrams of calcium')
    ],
    'objective_function': '8*x0 + 7*x1 + 7*x2',
    'constraints': [
        '9*x0 <= 284',
        '5*x0 <= 392',
        '13*x1 <= 284',
        '23*x1 <= 392',
        '12*x2 <= 284',
        '17*x2 <= 392',
        '9*x0 + 12*x2 >= 35',
        '9*x0 + 13*x1 + 12*x2 >= 56',
        '5*x0 + 17*x2 >= 112',
        '23*x1 + 17*x2 >= 79',
        '5*x0 + 23*x1 >= 112',
        '5*x0 + 23*x1 + 17*x2 >= 112',
        '-3*x1 + 8*x2 >= 0',
        '-3*x0 + 10*x2 >= 0',
        '13*x1 + 12*x2 <= 213',
        '9*x0 + 12*x2 <= 104',
        '9*x0 + 13*x1 + 12*x2 <= 255'
    ]
}
```

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

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

    # Define the variables
    x0 = model.addVar(name="x0", lb=0)  # milligrams of vitamin B9
    x1 = model.addVar(name="x1", lb=0)  # milligrams of magnesium
    x2 = model.addVar(name="x2", lb=0)  # milligrams of calcium

    # Define the objective function
    model.setObjective(8 * x0 + 7 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(9 * x0 <= 284)
    model.addConstr(5 * x0 <= 392)
    model.addConstr(13 * x1 <= 284)
    model.addConstr(23 * x1 <= 392)
    model.addConstr(12 * x2 <= 284)
    model.addConstr(17 * x2 <= 392)
    model.addConstr(9 * x0 + 12 * x2 >= 35)
    model.addConstr(9 * x0 + 13 * x1 + 12 * x2 >= 56)
    model.addConstr(5 * x0 + 17 * x2 >= 112)
    model.addConstr(23 * x1 + 17 * x2 >= 79)
    model.addConstr(5 * x0 + 23 * x1 >= 112)
    model.addConstr(5 * x0 + 23 * x1 + 17 * x2 >= 112)
    model.addConstr(-3 * x1 + 8 * x2 >= 0)
    model.addConstr(-3 * x0 + 10 * x2 >= 0)
    model.addConstr(13 * x1 + 12 * x2 <= 213)
    model.addConstr(9 * x0 + 12 * x2 <= 104)
    model.addConstr(9 * x0 + 13 * x1 + 12 * x2 <= 255)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B9: {x0.varValue}")
        print(f"Milligrams of magnesium: {x1.varValue}")
        print(f"Milligrams of calcium: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```