## Step 1: Define the symbolic representation of the variables
The variables are ['milligrams of zinc', 'milligrams of vitamin B2', 'milligrams of vitamin B3'], 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 and their corresponding natural language objects are:
- $x_0$ : milligrams of zinc
- $x_1$ : milligrams of vitamin B2
- $x_2$ : milligrams of vitamin B3

## 3: Define the objective function in symbolic notation
The objective function to minimize is $9x_0 + 4x_1 + 7x_2$.

## 4: List the constraints in symbolic notation
The constraints are:
- $9x_0 \leq 132$
- $7x_0 \leq 219$
- $6x_0 \leq 416$
- $19x_1 \leq 132$
- $19x_1 \leq 219$
- $9x_1 \leq 416$
- $3x_2 \leq 132$
- $16x_2 \leq 219$
- $19x_2 \leq 416$
- $9x_0 + 3x_2 \geq 32$
- $9x_0 + 19x_1 \geq 34$
- $9x_0 + 19x_1 + 3x_2 \geq 34$
- $7x_0 + 19x_1 \geq 69$
- $19x_1 + 16x_2 \geq 29$
- $7x_0 + 19x_1 + 16x_2 \geq 29$
- $9x_1 + 19x_2 \geq 97$
- $6x_0 + 19x_2 \geq 71$
- $6x_0 + 9x_1 + 19x_2 \geq 71$
- $8x_1 - 2x_2 \geq 0$
- $10x_0 - 4x_1 \geq 0$
- $9x_0 + 19x_1 + 3x_2 \leq 114$
- $7x_0 + 19x_1 \leq 122$
- $7x_0 + 16x_2 \leq 218$

## 5: Consider the variable bounds
- $x_0$ can be fractional
- $x_1$ must be an integer
- $x_2$ can be fractional

## 6: Write down the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'milligrams of zinc'), ('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of vitamin B3')],
    'objective_function': '9*x0 + 4*x1 + 7*x2',
    'constraints': [
        '9*x0 <= 132',
        '7*x0 <= 219',
        '6*x0 <= 416',
        '19*x1 <= 132',
        '19*x1 <= 219',
        '9*x1 <= 416',
        '3*x2 <= 132',
        '16*x2 <= 219',
        '19*x2 <= 416',
        '9*x0 + 3*x2 >= 32',
        '9*x0 + 19*x1 >= 34',
        '9*x0 + 19*x1 + 3*x2 >= 34',
        '7*x0 + 19*x1 >= 69',
        '19*x1 + 16*x2 >= 29',
        '7*x0 + 19*x1 + 16*x2 >= 29',
        '9*x1 + 19*x2 >= 97',
        '6*x0 + 19*x2 >= 71',
        '6*x0 + 9*x1 + 19*x2 >= 71',
        '8*x1 - 2*x2 >= 0',
        '10*x0 - 4*x1 >= 0',
        '9*x0 + 19*x1 + 3*x2 <= 114',
        '7*x0 + 19*x1 <= 122',
        '7*x0 + 16*x2 <= 218'
    ]
}
```

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

def optimize():
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(lb=0, name="milligrams_of_zinc", vtype=gurobi.GRB.CONTINUOUS)
    x1 = model.addVar(lb=0, name="milligrams_of_vitamin_B2", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(lb=0, name="milligrams_of_vitamin_B3", vtype=gurobi.GRB.CONTINUOUS)

    # Objective function
    model.setObjective(9 * x0 + 4 * x1 + 7 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(9 * x0 <= 132)
    model.addConstr(7 * x0 <= 219)
    model.addConstr(6 * x0 <= 416)
    model.addConstr(19 * x1 <= 132)
    model.addConstr(19 * x1 <= 219)
    model.addConstr(9 * x1 <= 416)
    model.addConstr(3 * x2 <= 132)
    model.addConstr(16 * x2 <= 219)
    model.addConstr(19 * x2 <= 416)
    model.addConstr(9 * x0 + 3 * x2 >= 32)
    model.addConstr(9 * x0 + 19 * x1 >= 34)
    model.addConstr(9 * x0 + 19 * x1 + 3 * x2 >= 34)
    model.addConstr(7 * x0 + 19 * x1 >= 69)
    model.addConstr(19 * x1 + 16 * x2 >= 29)
    model.addConstr(7 * x0 + 19 * x1 + 16 * x2 >= 29)
    model.addConstr(9 * x1 + 19 * x2 >= 97)
    model.addConstr(6 * x0 + 19 * x2 >= 71)
    model.addConstr(6 * x0 + 9 * x1 + 19 * x2 >= 71)
    model.addConstr(8 * x1 - 2 * x2 >= 0)
    model.addConstr(10 * x0 - 4 * x1 >= 0)
    model.addConstr(9 * x0 + 19 * x1 + 3 * x2 <= 114)
    model.addConstr(7 * x0 + 19 * x1 <= 122)
    model.addConstr(7 * x0 + 16 * x2 <= 218)

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of zinc: {x0.varValue}")
        print(f"Milligrams of vitamin B2: {x1.varValue}")
        print(f"Milligrams of vitamin B3: {x2.varValue}")
        print(f"Objective function value: {model.objVal}")
    else:
        print("No optimal solution found.")

optimize()
```