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

## Step 2: Translate the problem into a symbolic optimization problem
The objective function to minimize is $6.46x_0 + 1.98x_1 + 6.73x_2$.

However, based on the problem description, the objective function seems to be a product: $6.46x_0 \times 1.98x_1 \times 6.73x_2$ is not what is described; instead, we have $6.46 \times x_0 + 1.98 \times x_1 + 6.73 \times x_2$ does not match the description given. The correct interpretation of the objective function from the problem statement is $6.46x_0 + 1.98x_1 + 6.73x_2$ does not match; the actual description is to minimize $6.46x_0 \times 1.98x_1 \times 6.73x_2$ is also not correct based on the text. The problem states: "minimize the value of 6.46 times the total number of milligrams of vitamin D added to 1.98 multiplied by the quantity of milligrams of calcium added to 6.73 multiplied by the number of milligrams of vitamin B2", which translates to minimizing $6.46x_0 + 1.98x_1 + 6.73x_2$ is incorrect. The correct objective function based on the natural language description seems to actually be $6.46x_0 + 1.98x_1 + 6.73x_2$ is not it; it is actually about the product of the three variables multiplied by their respective factors which seems to be misinterpreted. The correct interpretation should be: minimize $(6.46x_0)(1.98x_1)(6.73x_2)$.

## 3: List all constraints
1. $23x_0 + 34x_1 + 33x_2 \geq 28$ (total cardiovascular support index from $x_0$, $x_1$, and $x_2$ but specifically $x_0$ and $x_2$ has to be 28 at minimum)
2. $23x_0 + 34x_1 \geq 51$ 
3. $23x_0 + 34x_1 + 33x_2 \geq 51$
4. $10x_0 + 9x_2 \geq 23$
5. $10x_0 + 11x_1 \geq 20$
6. $11x_1 + 9x_2 \geq 40$
7. $10x_0 + 11x_1 + 9x_2 \geq 46$
8. $23x_0 + 34x_1 + 33x_2 \leq 168$ (cardiovascular support index max)

## 4: Define the symbolic variables and constraints
$\textbf{sym\_variables:} [('x0', 'milligrams of vitamin D'), ('x1', 'milligrams of calcium'), ('x2', 'milligrams of vitamin B2')]$

$\textbf{objective\_function:} 6.46x_0 * 1.98x_1 * 6.73x_2$

$\textbf{constraints:}$
- $23x_0 + 33x_2 \geq 28$
- $23x_0 + 34x_1 \geq 51$
- $23x_0 + 34x_1 + 33x_2 \geq 51$
- $10x_0 + 9x_2 \geq 23$
- $10x_0 + 11x_1 \geq 20$
- $11x_1 + 9x_2 \geq 40$
- $10x_0 + 11x_1 + 9x_2 \geq 46$
- $23x_0 + 34x_1 + 33x_2 \leq 168$

## 5: Implement the problem in Gurobi
```python
import gurobi

def solve_optimization_problem():
    model = gurobi.Model()
    
    # Define variables
    x0 = model.addVar(name="x0", lb=0)  # milligrams of vitamin D
    x1 = model.addVar(name="x1", lb=0)  # milligrams of calcium
    x2 = model.addVar(name="x2", lb=0)  # milligrams of vitamin B2

    # Objective function
    model.setObjective(6.46 * x0 * 1.98 * x1 * 6.73 * x2, gurobi.GRB.MINIMIZE)

    # Constraints
    model.addConstr(23 * x0 + 33 * x2 >= 28)
    model.addConstr(23 * x0 + 34 * x1 >= 51)
    model.addConstr(23 * x0 + 34 * x1 + 33 * x2 >= 51)
    model.addConstr(10 * x0 + 9 * x2 >= 23)
    model.addConstr(10 * x0 + 11 * x1 >= 20)
    model.addConstr(11 * x1 + 9 * x2 >= 40)
    model.addConstr(10 * x0 + 11 * x1 + 9 * x2 >= 46)
    model.addConstr(23 * x0 + 34 * x1 + 33 * x2 <= 168)

    # Solve the model
    model.optimize()

    if model.status == gurobi.GRB.Status.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin D: {x0.varValue}")
        print(f"Milligrams of calcium: {x1.varValue}")
        print(f"Milligrams of vitamin B2: {x2.varValue}")
        print(f"Objective: {model.objVal}")
    elif model.status == gurobi.GRB.Status.INFEASIBLE:
        print("The model is infeasible.")
    else:
        print("The model has a non-optimal status.")

solve_optimization_problem()
```

## 6: Provide the final output in the required format
```json
{
    'sym_variables': [('x0', 'milligrams of vitamin D'), ('x1', 'milligrams of calcium'), ('x2', 'milligrams of vitamin B2')],
    'objective_function': '6.46*x0 * 1.98*x1 * 6.73*x2',
    'constraints': [
        '23*x0 + 33*x2 >= 28',
        '23*x0 + 34*x1 >= 51',
        '23*x0 + 34*x1 + 33*x2 >= 51',
        '10*x0 + 9*x2 >= 23',
        '10*x0 + 11*x1 >= 20',
        '11*x1 + 9*x2 >= 40',
        '10*x0 + 11*x1 + 9*x2 >= 46',
        '23*x0 + 34*x1 + 33*x2 <= 168'
    ]
}
```