## Step 1: Define the symbolic representation of the problem
The problem involves two variables: 'milligrams of vitamin B2' and 'milligrams of vitamin D'. Let's denote 'milligrams of vitamin B2' as $x_1$ and 'milligrams of vitamin D' as $x_2$. The objective function to minimize is $7x_1 + 3x_2$.

## 2: List the constraints in symbolic notation
The constraints given are:
- $6x_1 + 1x_2 \geq 21$
- $2x_1 + 2x_2 \geq 6$
- $4x_1 + 4x_2 \geq 17$
- $7x_1 - 6x_2 \geq 0$
- $6x_1 + 1x_2 \leq 39$
- $2x_1 + 2x_2 \leq 22$
- $4x_1 + 4x_2 \leq 23$
- $x_1$ can be fractional, $x_2$ can be fractional.

## 3: Provide the symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'milligrams of vitamin B2'), ('x2', 'milligrams of vitamin D')],
'objective_function': '7*x1 + 3*x2',
'constraints': [
    '6*x1 + 1*x2 >= 21',
    '2*x1 + 2*x2 >= 6',
    '4*x1 + 4*x2 >= 17',
    '7*x1 - 6*x2 >= 0',
    '6*x1 + 1*x2 <= 39',
    '2*x1 + 2*x2 <= 22',
    '4*x1 + 4*x2 <= 23'
]
}
```

## 4: Convert the problem into Gurobi code
Now, let's write the Gurobi code for this problem:
```python
import gurobi

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

    # Define the variables
    x1 = model.addVar(name="milligrams_of_vitamin_B2", lb=0)  # Assuming lb=0, no upper bound given
    x2 = model.addVar(name="milligrams_of_vitamin_D", lb=0)  # Assuming lb=0, no upper bound given

    # Define the objective function
    model.setObjective(7 * x1 + 3 * x2, gurobi.GRB.MINIMIZE)

    # Add constraints
    model.addConstr(6 * x1 + x2 >= 21, name="cardiovascular_support_index_min")
    model.addConstr(2 * x1 + 2 * x2 >= 6, name="energy_stability_index_min")
    model.addConstr(4 * x1 + 4 * x2 >= 17, name="cognitive_performance_index_min")
    model.addConstr(7 * x1 - 6 * x2 >= 0, name="vitamin_B2_D_constraint")
    model.addConstr(6 * x1 + x2 <= 39, name="cardiovascular_support_index_max")
    model.addConstr(2 * x1 + 2 * x2 <= 22, name="energy_stability_index_max")
    model.addConstr(4 * x1 + 4 * x2 <= 23, name="cognitive_performance_index_max")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        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()
```