To solve the optimization problem described, we first need to identify and translate all components into a symbolic representation that can be used in mathematical modeling. 

Given:
- Variables: milligrams of vitamin B7 and milligrams of vitamin B9.
- Objective Function: Maximize \(4.43 \times \text{milligrams of vitamin B7} + 3.0 \times \text{milligrams of vitamin B9}\).
- Constraints as described.

Let's denote:
- \(x_1\) as the milligrams of vitamin B7,
- \(x_2\) as the milligrams of vitamin B9.

The symbolic representation of the problem is thus:

```json
{
  'sym_variables': [('x1', 'milligrams of vitamin B7'), ('x2', 'milligrams of vitamin B9')],
  'objective_function': 'Maximize 4.43*x1 + 3.0*x2',
  'constraints': [
    '11*x1 + 14*x2 >= 49',  # Total combined energy stability index
    '12*x1 + 16*x2 >= 61',  # Total combined kidney support index
    '12*x1 + 4*x2 >= 47',   # Total combined cognitive performance index
    '-5*x1 + 2*x2 >= 0',    # Specific constraint involving both variables
    '11*x1 + 14*x2 <= 130', # Maximum total combined energy stability index
    '12*x1 + 16*x2 <= 118', # Maximum total combined kidney support index
    '12*x1 + 4*x2 <= 129'   # Maximum total combined cognitive performance index
  ]
}
```

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

```python
from gurobipy import *

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

# Add variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B7")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B9")

# Set the objective function
m.setObjective(4.43*x1 + 3.0*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(11*x1 + 14*x2 >= 49, "energy_stability_index")
m.addConstr(12*x1 + 16*x2 >= 61, "kidney_support_index")
m.addConstr(12*x1 + 4*x2 >= 47, "cognitive_performance_index")
m.addConstr(-5*x1 + 2*x2 >= 0, "specific_constraint")
m.addConstr(11*x1 + 14*x2 <= 130, "max_energy_stability_index")
m.addConstr(12*x1 + 16*x2 <= 118, "max_kidney_support_index")
m.addConstr(12*x1 + 4*x2 <= 129, "max_cognitive_performance_index")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Milligrams of Vitamin B7: {x1.x}")
    print(f"Milligrams of Vitamin B9: {x2.x}")
else:
    print("No optimal solution found")

```