```python
from gurobipy import Model, GRB, quicksum

# Define the model
model = Model("Vitamin Optimization")

# Define the variables
vitamin_b6 = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_b6")
vitamin_e = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_e")
vitamin_b9 = model.addVar(vtype=GRB.INTEGER, name="vitamin_b9")
fiber = model.addVar(vtype=GRB.CONTINUOUS, name="fiber")
iron = model.addVar(vtype=GRB.CONTINUOUS, name="iron")
vitamin_k = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_k")
vitamin_b5 = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_b5")

# Define the objective function
obj = 9.43 * vitamin_b6 + 6.8 * vitamin_e + 9.05 * vitamin_b9 + 6.01 * fiber + 4.84 * iron + 2.36 * vitamin_k + 7.56 * vitamin_b5
model.setObjective(obj, GRB.MINIMIZE)

# Define the resource constraints
resources = {
    'r0': {'upper_bound': 518, 'coeffs': [9, 10, 8, 3, 8, 6, 11]},  # energy stability index
    'r1': {'upper_bound': 588, 'coeffs': [3, 17, 10, 11, 14, 4, 7]},  # immune support index
    'r2': {'upper_bound': 227, 'coeffs': [4, 5, 16, 14, 12, 9, 10]}   # cognitive performance index
}

for r, data in resources.items():
    model.addConstr(
        quicksum(data['coeffs'][i] * var for i, var in enumerate([vitamin_b6, vitamin_e, vitamin_b9, fiber, iron, vitamin_k, vitamin_b5])) <= data['upper_bound'], name=f"{r}_ub"
    )


# Add the additional constraints provided in the input
model.addConstr(8 * iron + 6 * vitamin_k >= 44, "c1")
model.addConstr(9 * vitamin_b6 + 11 * vitamin_b5 >= 27, "c2")
model.addConstr(10 * vitamin_e + 8 * vitamin_b9 >= 27, "c3")
model.addConstr(8 * vitamin_b9 + 6 * vitamin_k >= 52, "c4")
model.addConstr(8 * vitamin_b9 + 8 * iron >= 52, "c5")
model.addConstr(6 * vitamin_k + 11 * vitamin_b5 >= 27, "c6")
model.addConstr(8 * iron + 11 * vitamin_b5 >= 24, "c7")
model.addConstr(8 * vitamin_b9 + 3 * fiber >= 33, "c8")
model.addConstr(10 * vitamin_e + 8 * vitamin_b9 + 11 * vitamin_b5 >= 64, "c9")
model.addConstr(9 * vitamin_b6 + 10 * vitamin_e + 8 * vitamin_b9 + 3 * fiber + 8 * iron + 6 * vitamin_k + 11 * vitamin_b5 >= 64, "c10")
model.addConstr(11 * fiber + 7 * vitamin_b5 >= 48, "c11")
model.addConstr(4 * vitamin_k + 7 * vitamin_b5 >= 58, "c12")
model.addConstr(10 * vitamin_b9 + 7 * vitamin_b5 >= 63, "c13")
model.addConstr(3 * vitamin_b6 + 10 * vitamin_b9 >= 51, "c14")
model.addConstr(14 * iron + 7 * vitamin_b5 >= 67, "c15")
model.addConstr(11 * fiber + 4 * vitamin_k >= 73, "c16")
model.addConstr(3 * vitamin_b6 + 4 * vitamin_k >= 57, "c17")
model.addConstr(17 * vitamin_e + 4 * vitamin_k >= 79, "c18")
model.addConstr(17 * vitamin_e + 7 * vitamin_b5 >= 56, "c19")
model.addConstr(14 * iron + 4 * vitamin_k >= 62, "c20")
model.addConstr(10 * vitamin_b9 + 4 * vitamin_k >= 58, "c21")
model.addConstr(17 * vitamin_e + 11 * fiber >= 59, "c22")
model.addConstr(11 * fiber + 14 * iron >= 83, "c23")
model.addConstr(3 * vitamin_b6 + 14 * iron >= 50, "c24")
model.addConstr(17 * vitamin_e + 11 * fiber + 7 * vitamin_b5 >= 82, "c25")
model.addConstr(3 * vitamin_b6 + 17 * vitamin_e + 10 * vitamin_b9 + 11 * fiber + 14 * iron + 4 * vitamin_k + 7 * vitamin_b5 >= 82, "c26")


# ... (The rest of the constraints and variable declarations as in the previous response)

# Optimize the model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    for v in model.getVars():
        print(f"{v.varName}: {v.x}")
    print(f"Objective value: {model.objVal}")
elif model.status == GRB.INFEASIBLE:
    print("Model is infeasible.")
else:
    print(f"Optimization terminated with status: {model.status}")

```