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

Let's define:
- $x_1$ as the milligrams of vitamin K,
- $x_2$ as the milligrams of vitamin B6.

The objective function to minimize is: $3x_1 + 6x_2$

Given the constraints:
1. Digestive support index for $x_1$ is 9.
2. Energy stability index for $x_1$ is 9.
3. Cognitive performance index for $x_1$ is 11.
4. Digestive support index for $x_2$ is 8.
5. Energy stability index for $x_2$ is 20.
6. Cognitive performance index for $x_2$ is 17.
7. Total combined digestive support index $\geq 28$.
8. Total combined energy stability index $\geq 13$.
9. Total combined cognitive performance index $\geq 29$.
10. $-7x_1 + 9x_2 \geq 0$.
11. Total combined digestive support index $\leq 57$.
12. Total combined energy stability index $\leq 49$.
13. Total combined cognitive performance index $\leq 59$.

These can be represented symbolically as:
```json
{
    'sym_variables': [('x1', 'milligrams of vitamin K'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '3*x1 + 6*x2',
    'constraints': [
        '9*x1 + 8*x2 >= 28', 
        '9*x1 + 20*x2 >= 13', 
        '11*x1 + 17*x2 >= 29', 
        '-7*x1 + 9*x2 >= 0',
        '9*x1 + 8*x2 <= 57', 
        '9*x1 + 20*x2 <= 49', 
        '11*x1 + 17*x2 <= 59'
    ]
}
```

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

```python
from gurobipy import *

# Create a new model
m = Model("Vitamins_Optimization")

# Create variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_K")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="milligrams_of_vitamin_B6")

# Set the objective function
m.setObjective(3*x1 + 6*x2, GRB.MINIMIZE)

# Add constraints
m.addConstr(9*x1 + 8*x2 >= 28, "digestive_support_index_min")
m.addConstr(9*x1 + 20*x2 >= 13, "energy_stability_index_min")
m.addConstr(11*x1 + 17*x2 >= 29, "cognitive_performance_index_min")
m.addConstr(-7*x1 + 9*x2 >= 0, "vitamin_balance")
m.addConstr(9*x1 + 8*x2 <= 57, "digestive_support_index_max")
m.addConstr(9*x1 + 20*x2 <= 49, "energy_stability_index_max")
m.addConstr(11*x1 + 17*x2 <= 59, "cognitive_performance_index_max")

# Optimize the model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print("Milligrams of Vitamin K:", x1.x)
    print("Milligrams of Vitamin B6:", x2.x)
    print("Objective Function Value:", m.objVal)
else:
    print("No optimal solution found")
```