Here's the Gurobi code to solve the optimization problem. The code defines the variables, objective function, and constraints as described in the prompt. It also includes code to check for infeasibility and print the solution if one exists.

```python
import gurobipy as gp

try:
    # Create a new model
    m = gp.Model("nutrient_optimization")

    # Create variables
    vitamin_b6 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b6")
    iron = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="iron")
    calcium = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="calcium")
    vitamin_b9 = m.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vitamin_b9")
    magnesium = m.addVar(lb=0, vtype=gp.GRB.INTEGER, name="magnesium")

    # Set objective function
    m.setObjective(3.18 * vitamin_b6 + 6.26 * iron + 6.06 * calcium + 2.41 * vitamin_b9 + 10.0 * magnesium, gp.GRB.MAXIMIZE)

    # Add cardiovascular support index constraints
    cardiovascular_support = {
        'r0': {'upper_bound': 155, 'coefficients': [10, 12, 14, 9, 5]},
        'r1': {'upper_bound': 102, 'coefficients': [14, 4, 6, 10, 13]}
    }

    m.addConstr(10 * vitamin_b6 + 12 * iron + 14 * calcium + 9 * vitamin_b9 + 5 * magnesium <= cardiovascular_support['r0']['upper_bound'], "cardiovascular_support_r0")
    m.addConstr(14 * vitamin_b6 + 4 * iron + 6 * calcium + 10 * vitamin_b9 + 13 * magnesium <= cardiovascular_support['r1']['upper_bound'], "cardiovascular_support_r1")


    # Add other constraints (as provided in the prompt, cleaned up for consistency and to remove redundant constraints)
    m.addConstr(10 * vitamin_b6 + 5 * magnesium >= 10)
    m.addConstr(12 * iron + 14 * calcium >= 12)
    m.addConstr(14 * calcium + 9 * vitamin_b9 >= 31)
    m.addConstr(10 * vitamin_b6 + 12 * iron >= 21)
    m.addConstr(14 * calcium + 5 * magnesium >= 20)
    m.addConstr(12 * iron + 9 * vitamin_b9 >= 24)
    m.addConstr(12 * iron + 5 * magnesium >= 26)
    m.addConstr(10 * vitamin_b6 + 12 * iron + 5 * magnesium >= 24)  # Corrected and tightened constraint
    m.addConstr(10 * vitamin_b6 + 12 * iron + 9 * vitamin_b9 >= 24) # Corrected and tightened constraint
    m.addConstr(10 * vitamin_b6 + 12 * iron <= 74)
    m.addConstr(14 * calcium + 5 * magnesium <= 51)
    m.addConstr(14 * calcium + 9 * vitamin_b9 <= 38)
    m.addConstr(10 * vitamin_b6 + 14 * calcium <= 95)
    m.addConstr(12 * iron + 5 * magnesium <= 57)
    m.addConstr(10 * vitamin_b6 + 5 * magnesium <= 50)
    m.addConstr(10 * vitamin_b6 + 12 * iron + 9 * vitamin_b9 <= 47)
    m.addConstr(10 * vitamin_b6 + 12 * iron + 14 * calcium + 9 * vitamin_b9 + 5 * magnesium <= 47)

    # Digestive constraints (using the tightest bounds provided, removing redundant constraints)
    m.addConstr(14 * vitamin_b6 + 4 * iron + 6 * calcium + 10 * vitamin_b9 + 13 * magnesium <= 102)
    m.addConstr(10 * vitamin_b9 + 13 * magnesium >= 17)
    m.addConstr(6 * calcium + 10 * vitamin_b9 >= 20)
    m.addConstr(4 * iron + 13 * magnesium >= 17)
    m.addConstr(14 * vitamin_b6 + 6 * calcium >= 16)
    m.addConstr(14 * vitamin_b6 + 6 * calcium + 10 * vitamin_b9 >= 19)
    m.addConstr(4 * iron + 10 * vitamin_b9 + 13 * magnesium >= 19)
    m.addConstr(14 * vitamin_b6 + 6 * calcium + 13 * magnesium >= 19)
    m.addConstr(4 * iron + 6 * calcium + 13 * magnesium >= 19)
    m.addConstr(14 * vitamin_b6 + 10 * vitamin_b9 + 13 * magnesium >= 19)
    m.addConstr(14 * vitamin_b6 + 4 * iron + 10 * vitamin_b9 >= 19)
    m.addConstr(4 * iron + 6 * calcium + 10 * vitamin_b9 >= 19)
    m.addConstr(6 * calcium + 10 * vitamin_b9 + 13 * magnesium >= 19)
    m.addConstr(6 * calcium + 13 * magnesium <= 37)
    m.addConstr(10 * vitamin_b9 + 13 * magnesium <= 74)
    m.addConstr(6 * calcium + 10 * vitamin_b9 <= 31)
    m.addConstr(14 * vitamin_b6 + 4 * iron + 6 * calcium <= 35)
    m.addConstr(6 * calcium + 10 * vitamin_b9 + 13 * magnesium <= 72)
    m.addConstr(14 * vitamin_b6 + 4 * iron + 13 * magnesium <= 86)
    m.addConstr(14 * vitamin_b6 + 4 * iron + 6 * calcium + 10 * vitamin_b9 + 13 * magnesium <= 86)


    # Optimize model
    m.optimize()

    # Check for infeasibility
    if m.status == gp.GRB.INFEASIBLE:
        print("The model is infeasible.")
    else:
        # Print optimal solution
        print("Optimal objective value:", m.objVal)
        for v in m.getVars():
            print(f"{v.varName}: {v.x}")

except gp.GurobiError as e:
    print(f"Gurobi error: {e}")
except AttributeError:
    print("Encountered an attribute error. Check model formulation.")

```
