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

**Decision Variables:**

*  `x`: Number of small containers produced.
*  `y`: Number of bulk containers produced.

**Objective Function:**

Maximize profit: `2x + 7y`

**Constraints:**

* **Fish food constraint:** `10x + 30y <= 200` (Total fish food used cannot exceed available fish food)
* **Time constraint:** `2x + 7y <= 120` (Total filling time cannot exceed available filling time)
* **Non-negativity constraints:** `x >= 0`, `y >= 0` (Cannot produce a negative number of containers)


```python
import gurobipy as gp

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

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

# Set objective function
model.setObjective(2*x + 7*y, gp.GRB.MAXIMIZE)

# Add constraints
model.addConstr(10*x + 30*y <= 200, "fish_food_constraint")
model.addConstr(2*x + 7*y <= 120, "time_constraint")

# Optimize the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal profit: ${model.objVal:.2f}")
    print(f"Number of small containers: {x.x:.2f}")
    print(f"Number of bulk containers: {y.x:.2f}")
elif model.status == gp.GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization ended with status {model.status}")

```
