To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation. The variables are 'grams of carbohydrates' and 'milligrams of vitamin B6', which we'll denote as `x1` and `x2`, respectively.

The objective function is to maximize: 
\[ 6.63x_1^2 + 7.63x_1x_2 + 4.72x_2^2 + 3.6x_1 + 8.16x_2 \]

Given the constraints:
- The cognitive performance index of grams of carbohydrates is 1, and for milligrams of vitamin B6 is 6.
- The kidney support index of grams of carbohydrates is 2, and for milligrams of vitamin B6 is 1.
- The total combined cognitive performance index from grams of carbohydrates squared and milligrams of vitamin B6 squared must be greater than or equal to 32: \( x_1^2 + 36x_2^2 \geq 32 \)
- The total combined kidney support index from grams of carbohydrates squared plus milligrams of vitamin B6 squared has to be as much or more than 33: \( 4x_1^2 + x_2^2 \geq 33 \)
- Eight times the number of grams of carbohydrates, plus -7 times the number of milligrams of vitamin B6 must be at minimum zero: \( 8x_1 - 7x_2 \geq 0 \)
- The total combined cognitive performance index from grams of carbohydrates plus milligrams of vitamin B6 has to be 78 or less: \( x_1 + 6x_2 \leq 78 \)
- The total combined kidney support index from grams of carbohydrates and milligrams of vitamin B6 must be 54 at a maximum: \( 2x_1 + x_2 \leq 54 \)
- There does not have to be a whole number amount of grams of carbohydrates.
- There has to be a whole number of milligrams of vitamin B6.

Here is the symbolic representation:
```json
{
    'sym_variables': [('x1', 'grams of carbohydrates'), ('x2', 'milligrams of vitamin B6')],
    'objective_function': '6.63*x1**2 + 7.63*x1*x2 + 4.72*x2**2 + 3.6*x1 + 8.16*x2',
    'constraints': [
        'x1**2 + 36*x2**2 >= 32', 
        '4*x1**2 + x2**2 >= 33', 
        '8*x1 - 7*x2 >= 0', 
        'x1 + 6*x2 <= 78', 
        '2*x1 + x2 <= 54'
    ]
}
```

To solve this problem using Gurobi, we'll write the following Python code:

```python
from gurobipy import *

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

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

# Set the objective function
m.setObjective(6.63*x1**2 + 7.63*x1*x2 + 4.72*x2**2 + 3.6*x1 + 8.16*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1**2 + 36*x2**2 >= 32, "cognitive_performance_index")
m.addConstr(4*x1**2 + x2**2 >= 33, "kidney_support_index_squared")
m.addConstr(8*x1 - 7*x2 >= 0, "carbohydrates_vitamin_b6_ratio")
m.addConstr(x1 + 6*x2 <= 78, "total_cognitive_performance")
m.addConstr(2*x1 + x2 <= 54, "total_kidney_support")

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of carbohydrates: {x1.x}")
    print(f"Milligrams of vitamin B6: {x2.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found.")
```