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 then solves the model and prints the optimal solution if one exists, or indicates if the model is infeasible.

```python
from gurobipy import Model, GRB

# Create a new model
model = Model("optimization_problem")

# Create variables
vitamin_b7 = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_b7")
fat = model.addVar(vtype=GRB.CONTINUOUS, name="fat")
vitamin_k = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_k")
vitamin_b2 = model.addVar(vtype=GRB.CONTINUOUS, name="vitamin_b2")

# Set objective function
model.setObjective(9 * vitamin_b7 + 1 * fat + 4 * vitamin_k + 6 * vitamin_b2, GRB.MINIMIZE)

# Add constraints
model.addConstr(6 * fat + 6 * vitamin_b2 >= 39, "c1")
model.addConstr(5 * fat + 5 * vitamin_k >= 19, "c2")
model.addConstr(6 * vitamin_b7 + 5 * fat >= 27, "c3")
model.addConstr(6 * vitamin_b7 + 5 * vitamin_k >= 22, "c4")
model.addConstr(5 * vitamin_k + 6 * vitamin_b2 >= 18, "c5")
model.addConstr(6 * vitamin_b7 + 5 * fat + 6 * vitamin_b2 >= 33, "c6")
model.addConstr(6 * vitamin_b7 + 5 * fat + 5 * vitamin_k + 6 * vitamin_b2 >= 33, "c7")
model.addConstr(2 * vitamin_b7 + 13 * vitamin_k >= 25, "c8")
model.addConstr(12 * fat + 11 * vitamin_b2 >= 22, "c9")
model.addConstr(13 * vitamin_k + 11 * vitamin_b2 >= 34, "c10")
model.addConstr(12 * fat + 13 * vitamin_k >= 21, "c11")
model.addConstr(2 * vitamin_b7 + 12 * fat >= 35, "c12")
model.addConstr(2 * vitamin_b7 + 12 * fat + 11 * vitamin_b2 >= 21, "c13")
model.addConstr(2 * vitamin_b7 + 13 * vitamin_k + 11 * vitamin_b2 >= 21, "c14")
model.addConstr(2 * vitamin_b7 + 12 * fat + 13 * vitamin_k >= 21, "c15")
model.addConstr(2 * vitamin_b7 + 12 * fat + 11 * vitamin_b2 >= 17, "c16")
model.addConstr(2 * vitamin_b7 + 13 * vitamin_k + 11 * vitamin_b2 >= 17, "c17")
model.addConstr(2 * vitamin_b7 + 12 * fat + 13 * vitamin_k >= 17, "c18")
model.addConstr(2 * vitamin_b7 + 12 * fat + 11 * vitamin_b2 >= 35, "c19")
model.addConstr(2 * vitamin_b7 + 13 * vitamin_k + 11 * vitamin_b2 >= 35, "c20")
model.addConstr(2 * vitamin_b7 + 12 * fat + 13 * vitamin_k >= 35, "c21")
model.addConstr(2 * vitamin_b7 + 12 * fat + 13 * vitamin_k + 11 * vitamin_b2 >= 35, "c22")
model.addConstr(2 * vitamin_b7 + 3 * vitamin_k >= 21, "c23")
model.addConstr(8 * fat + 3 * vitamin_k >= 20, "c24")
model.addConstr(2 * vitamin_b7 + 5 * vitamin_b2 >= 16, "c25")
model.addConstr(8 * fat + 5 * vitamin_b2 >= 17, "c26")
model.addConstr(2 * vitamin_b7 + 8 * fat >= 20, "c27")
model.addConstr(2 * vitamin_b7 + 8 * fat + 3 * vitamin_k + 5 * vitamin_b2 >= 20, "c28")
model.addConstr(6 * fat - 9 * vitamin_b2 >= 0, "c29")

#Resource Upper Bound Constraints
model.addConstr(6 * vitamin_b7 + 5 * fat + 5 * vitamin_k + 6 * vitamin_b2 <= 195, "r0")
model.addConstr(2 * vitamin_b7 + 12 * fat + 13 * vitamin_k + 11 * vitamin_b2 <= 140, "r1")
model.addConstr(2 * vitamin_b7 + 8 * fat + 3 * vitamin_k + 5 * vitamin_b2 <= 87, "r2")


#Additional Constraints from prompt
model.addConstr(6 * vitamin_b7 + 6 * vitamin_b2 <= 156, "c30")
model.addConstr(5 * fat + 6 * vitamin_b2 <= 139, "c31")
model.addConstr(5 * vitamin_k + 6 * vitamin_b2 <= 167, "c32")
model.addConstr(6 * vitamin_b7 + 5 * fat <= 104, "c33")
model.addConstr(6 * vitamin_b7 + 5 * vitamin_k <= 161, "c34")
model.addConstr(5 * fat + 5 * vitamin_k <= 177, "c35")
model.addConstr(5 * fat + 5 * vitamin_k + 6 * vitamin_b2 <= 123, "c36")
model.addConstr(2 * vitamin_b7 + 5 * vitamin_b2 <= 86, "c37")
model.addConstr(3 * vitamin_k + 5 * vitamin_b2 <= 23, "c38")
model.addConstr(2 * vitamin_b7 + 8 * fat <= 63, "c39")
model.addConstr(8 * fat + 3 * vitamin_k <= 57, "c40")
model.addConstr(8 * fat + 5 * vitamin_b2 <= 87, "c41")
model.addConstr(2 * vitamin_b7 + 3 * vitamin_k <= 64, "c42")
model.addConstr(2 * vitamin_b7 + 3 * vitamin_k + 5 * vitamin_b2 <= 45, "c43")
model.addConstr(2 * vitamin_b7 + 8 * fat + 3 * vitamin_k <= 66, "c44")



# Optimize model
model.optimize()

# Print results
if model.status == GRB.OPTIMAL:
    print('Objective Value:', model.objVal)
    for v in model.getVars():
        print(f'{v.varName}: {v.x}')
elif model.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")

```
