Here's the formulation of the linear program and the Gurobi code to solve it:

**Decision Variables:**

* `x`: Number of fruit smoothies made
* `y`: Number of vegetable smoothies made

**Objective Function:**

Maximize profit: `4x + 5y`

**Constraints:**

* Cutting machine time: `5x + 7y <= 400`
* Blending machine time: `5x + 4y <= 400`
* Non-negativity: `x >= 0`, `y >= 0`


```python
import gurobipy as gp

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

# Create decision variables
x = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="fruit_smoothies")
y = model.addVar(lb=0, vtype=gp.GRB.CONTINUOUS, name="vegetable_smoothies")

# Set objective function
model.setObjective(4*x + 5*y, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(5*x + 7*y <= 400, "cutting_constraint")
model.addConstr(5*x + 4*y <= 400, "blending_constraint")

# Optimize model
model.optimize()

# Print results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal Solution Found:")
    print(f"Fruit Smoothies (x): {x.x}")
    print(f"Vegetable Smoothies (y): {y.x}")
    print(f"Optimal Profit: {model.objVal}")
elif model.status == gp.GRB.INFEASIBLE:
    print("Model is infeasible.")
else:
    print(f"Optimization terminated with status: {model.status}")

```
