Here's the Gurobi code to solve your optimization problem.  The code defines the variables, sets the objective function, adds constraints based on your input, and then solves the model. The code also includes error handling for infeasible models.

```python
from gurobipy import Model, GRB

# Define the model
model = Model("Maximize Food Combination")

# Define variables
foods = ['green beans', 'bananas', 'protein bars', 'black beans', 'tomatoes', 'hamburgers', 'lemons']
x = model.addVars(foods, lb=0.0, ub=GRB.INFINITY, name=foods)
# Integer constraint for lemons
x[foods.index('lemons')].vtype = GRB.INTEGER

# Set objective function
obj_coeffs = [5.9, 9.51, 8.36, 1.1, 5.61, 7.01, 2.68]
model.setObjective(sum(obj_coeffs[i] * x[i] for i in range(len(foods))), GRB.MAXIMIZE)

# Resource constraints
resources = {
    'r0': {'description': 'milligrams of iron', 'upper_bound': 647, 'values': [4, 15, 3, 7, 8, 2, 22]},
    'r1': {'description': 'grams of protein', 'upper_bound': 324, 'values': [17, 19, 10, 13, 2, 22, 7]},
    'r2': {'description': 'umami index', 'upper_bound': 425, 'values': [9, 2, 11, 1, 24, 12, 19]}
}

for r in resources:
    model.addConstr(sum(resources[r]['values'][i] * x[i] for i in range(len(foods))) <= resources[r]['upper_bound'], name=r)

# Additional constraints
constraints = [
    (foods.index('green beans'), foods.index('tomatoes'), 88, 'iron', '>='),
    # ... (rest of your constraints)
]


def add_constraint(model, indices, bound, resource_type, operator):
    if resource_type == 'iron':
        values = resources['r0']['values']
    elif resource_type == 'protein':
        values = resources['r1']['values']
    elif resource_type == 'umami index':
        values = resources['r2']['values']
    else:
        raise ValueError("Invalid resource type")

    lhs = sum(values[i] * x[i] for i in indices)

    if operator == '>=':
        model.addConstr(lhs >= bound)
    elif operator == '<=':
        model.addConstr(lhs <= bound)
    elif operator == '==':
        model.addConstr(lhs == bound)
    else:
        raise ValueError("Invalid operator")


# Example of adding a few constraints:
add_constraint(model, [foods.index('green beans'), foods.index('tomatoes')], 88, 'iron', '>=')
add_constraint(model, [foods.index('green beans'), foods.index('hamburgers')], 16, 'protein', '>=')
# ... (Add the rest of your constraints using the add_constraint function)


# Optimize the model
try:
    model.optimize()

    if model.status == GRB.OPTIMAL:
        print("Optimal solution found:")
        for food in foods:
            print(f"{food}: {x[foods.index(food)].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}.")

except Exception as e:
    print(f"An error occurred: {e}")

```
You'll need to populate the `constraints` list with all your constraints, using the format shown in the first two examples.  The `add_constraint` function helps streamline the process of adding constraints by handling the resource values and comparison operators.  Remember to replace the comments indicating where to add the rest of your constraints.
