Here's the Gurobi code for the optimization problem:

```python
import gurobipy as gp

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

# Create variables
carbohydrates = m.addVar(lb=0, name="carbohydrates")
vitamin_b7 = m.addVar(lb=0, name="vitamin_b7")
vitamin_d = m.addVar(lb=0, name="vitamin_d")
vitamin_b1 = m.addVar(lb=0, name="vitamin_b1")
iron = m.addVar(lb=0, name="iron")

# Set objective function
m.setObjective(3.08 * carbohydrates + 2.45 * vitamin_b7 + 7.69 * vitamin_d + 5.19 * vitamin_b1 + 9.29 * iron, gp.GRB.MINIMIZE)

# Add constraints
kidney_support = {"carbohydrates": 4, "vitamin_b7": 1, "vitamin_d": 2, "vitamin_b1": 2, "iron": 6}
m.addConstr(kidney_support["vitamin_b1"] * vitamin_b1 + kidney_support["iron"] * iron >= 15, "c1")
m.addConstr(kidney_support["carbohydrates"] * carbohydrates + kidney_support["vitamin_b1"] * vitamin_b1 >= 7, "c2")
m.addConstr(kidney_support["carbohydrates"] * carbohydrates + kidney_support["iron"] * iron >= 18, "c3")
m.addConstr(kidney_support["vitamin_d"] * vitamin_d + kidney_support["vitamin_b1"] * vitamin_b1 >= 16, "c4")
m.addConstr(kidney_support["vitamin_b7"] * vitamin_b7 + kidney_support["vitamin_b1"] * vitamin_b1 + kidney_support["iron"] * iron >= 20, "c5")
m.addConstr(kidney_support["vitamin_d"] * vitamin_d + kidney_support["vitamin_b1"] * vitamin_b1 + kidney_support["iron"] * iron >= 20, "c6")
m.addConstr(kidney_support["vitamin_b7"] * vitamin_b7 + kidney_support["vitamin_d"] * vitamin_d + kidney_support["iron"] * iron >= 20, "c7")
m.addConstr(kidney_support["vitamin_b7"] * vitamin_b7 + kidney_support["vitamin_d"] * vitamin_d + kidney_support["vitamin_b1"] * vitamin_b1 >= 20, "c8")


m.addConstr(-10 * vitamin_b7 + 9 * iron >= 0, "c9")
m.addConstr(2 * vitamin_b7 - 9 * vitamin_b1 >= 0, "c10")


m.addConstr(kidney_support["carbohydrates"] * carbohydrates + kidney_support["vitamin_b7"] * vitamin_b7 + kidney_support["vitamin_d"] * vitamin_d + kidney_support["vitamin_b1"] * vitamin_b1 + kidney_support["iron"] * iron >= 10, "c11")


# Optimize model
m.optimize()

# Print results
if m.status == gp.GRB.OPTIMAL:
    print('Obj: %g' % m.objVal)
    for v in m.getVars():
        print('%s %g' % (v.varName, v.x))
elif m.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print("Optimization ended with status %d" % m.status)

```
