## Problem Description and Formulation

The problem is an optimization problem where we need to maximize the objective function: 2 * (milligrams of vitamin B2) + 2 * (milligrams of iron), subject to several constraints.

### Variables

* `x0`: milligrams of vitamin B2
* `x1`: milligrams of iron

### Constraints

* Kidney support index for `x0` is 24
* Cardiovascular support index for `x0` is 20
* Kidney support index for `x1` is 19
* Cardiovascular support index for `x1` is 10
* Total combined kidney support index: 24 * `x0` + 19 * `x1` >= 63
* Total combined cardiovascular support index: 20 * `x0` + 10 * `x1` >= 107
* Linear constraint: -8 * `x0` + 6 * `x1` >= 0
* Total combined kidney support index: 24 * `x0` + 19 * `x1` <= 238
* Total combined cardiovascular support index: 20 * `x0` + 10 * `x1` <= 254
* `x0` is continuous
* `x1` is integer

## Gurobi Code

```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define variables
    x0 = model.addVar(lb=-gurobi.GRB.INFINITY, ub=gurobi.GRB.INFINITY, name="milligrams of vitamin B2")
    x1 = model.addVar(lb=0, ub=gurobi.GRB.INFINITY, vtype=gurobi.GRB.INTEGER, name="milligrams of iron")

    # Objective function
    model.setObjective(2 * x0 + 2 * x1, gurobi.GRB.MAXIMIZE)

    # Constraints
    model.addConstr(24 * x0 + 19 * x1 >= 63, name="kidney_support_index")
    model.addConstr(20 * x0 + 10 * x1 >= 107, name="cardiovascular_support_index")
    model.addConstr(-8 * x0 + 6 * x1 >= 0, name="linear_constraint")
    model.addConstr(24 * x0 + 19 * x1 <= 238, name="kidney_support_index_upper_bound")
    model.addConstr(20 * x0 + 10 * x1 <= 254, name="cardiovascular_support_index_upper_bound")

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Milligrams of vitamin B2: {x0.varValue}")
        print(f"Milligrams of iron: {x1.varValue}")
        print(f"Objective function value: {model.objVal}")
    elif model.status == gurobi.GRB.INFEASIBLE:
        print("No feasible solution found.")
    else:
        print("Optimization failed.")

solve_optimization_problem()
```