Here's the Gurobi code to solve the optimization problem:

```python
import gurobipy as gp

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

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

# Set objective function
m.setObjective(9 * calcium + 2 * protein + 8 * vitamin_b9, gp.GRB.MAXIMIZE)

# Add constraints
m.addConstr(6.48 * protein + 3.53 * vitamin_b9 >= 47, "c1")
m.addConstr(7.69 * calcium + 6.48 * protein + 3.53 * vitamin_b9 >= 64, "c2")
m.addConstr(6.48 * protein + 3.53 * vitamin_b9 <= 157, "c3")
m.addConstr(7.69 * calcium + 3.53 * vitamin_b9 <= 158, "c4")
m.addConstr(7.69 * calcium + 6.48 * protein <= 192, "c5")
m.addConstr(7.69 * calcium + 6.48 * protein + 3.53 * vitamin_b9 <= 192, "c6")


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    print('calcium:', calcium.x)
    print('protein:', protein.x)
    print('vitamin_b9:', vitamin_b9.x)
elif m.status == gp.GRB.INFEASIBLE:
    print('The model is infeasible.')
else:
    print('Optimization ended with status %d' % m.status)

```
