To solve the optimization problem described, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints.

Let's denote:
- $x_1$ as the quantity of milligrams of vitamin B2,
- $x_2$ as the quantity of milligrams of vitamin D.

The objective function is given as: Minimize $7x_1 + 3x_2$.

The constraints are as follows:
1. Cardiovascular support index constraint: $6x_1 + x_2 \geq 21$
2. Energy stability index constraint: $2x_1 + 2x_2 \geq 6$
3. Cognitive performance index constraint: $4x_1 + 4x_2 \geq 17$
4. Constraint on vitamin B2 and D relationship: $7x_1 - 6x_2 \geq 0$
5. Upper bound on cardiovascular support index: $6x_1 + x_2 \leq 39$
6. Upper bound on energy stability index: $2x_1 + 2x_2 \leq 22$
7. Upper bound on cognitive performance index: $4x_1 + 4x_2 \leq 23$

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

Now, let's implement this problem using Gurobi in Python:

```python
from gurobipy import *

# Create a model
m = Model("Vitamin_Optimization")

# Define variables
x1 = m.addVar(name="milligrams_of_vitamin_B2", lb=0)
x2 = m.addVar(name="milligrams_of_vitamin_D", lb=0)

# Objective function
m.setObjective(7*x1 + 3*x2, GRB.MINIMIZE)

# Constraints
m.addConstr(6*x1 + x2 >= 21, name="cardiovascular_support_index")
m.addConstr(2*x1 + 2*x2 >= 6, name="energy_stability_index")
m.addConstr(4*x1 + 4*x2 >= 17, name="cognitive_performance_index")
m.addConstr(7*x1 - 6*x2 >= 0, name="vitamin_B2_and_D_relationship")
m.addConstr(6*x1 + x2 <= 39, name="upper_bound_cardiovascular_support")
m.addConstr(2*x1 + 2*x2 <= 22, name="upper_bound_energy_stability")
m.addConstr(4*x1 + 4*x2 <= 23, name="upper_bound_cognitive_performance")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B2: {x1.x}")
    print(f"Milligrams of Vitamin D: {x2.x}")
    print(f"Objective Function Value: {m.objVal}")
else:
    print("No optimal solution found")
```