To solve the given optimization problem using Gurobi, we need to define the variables, objective function, and constraints according to the provided description. The variables are 'grams of fat' (x0) and 'milligrams of calcium' (x1). The objective is to maximize 2 times the number of grams of fat plus 6 times the quantity of milligrams of calcium.

The constraints can be summarized as follows:
- The total combined cardiovascular support index from grams of fat and milligrams of calcium must be at least 42.
- The total combined energy stability index from grams of fat and milligrams of calcium must be at least 49.
- 9 times the number of grams of fat, minus 8 times the number of milligrams of calcium, must be greater than or equal to zero.
- The total combined cardiovascular support index should not exceed 88.
- The total combined energy stability index should not exceed 62.

Here's how we can translate these requirements into Gurobi code:

```python
from gurobipy import *

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

# Define the variables
x0 = m.addVar(vtype=GRB.CONTINUOUS, name='grams_of_fat')
x1 = m.addVar(vtype=GRB.CONTINUOUS, name='milligrams_of_calcium')

# Objective function: Maximize 2*x0 + 6*x1
m.setObjective(2*x0 + 6*x1, GRB.MAXIMIZE)

# Constraints
# Total combined cardiovascular support index >= 42
m.addConstr(3*x0 + 10*x1 >= 42, name='cardiovascular_support_min')

# Total combined energy stability index >= 49
m.addConstr(14*x0 + x1 >= 49, name='energy_stability_min')

# 9*x0 - 8*x1 >= 0
m.addConstr(9*x0 - 8*x1 >= 0, name='fat_calcium_ratio')

# Total combined cardiovascular support index <= 88
m.addConstr(3*x0 + 10*x1 <= 88, name='cardiovascular_support_max')

# Total combined energy stability index <= 62
m.addConstr(14*x0 + x1 <= 62, name='energy_stability_max')

# Optimize the model
m.optimize()

# Print the solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of fat: {x0.x}")
    print(f"Milligrams of calcium: {x1.x}")
    print(f"Objective value: {m.objVal}")
else:
    print("No optimal solution found")
```