Here's how we can formulate this problem and generate the Gurobi code:

**Decision Variables:**

* `x`: Investment in Bob's farm (in dollars)
* `y`: Investment in Joe's farm (in dollars)

**Objective Function:**

Maximize total earnings: `0.08x + 0.06y`

**Constraints:**

* **Total investment:** `x + y <= 50000`
* **Investment ratio:** `x >= 3y`
* **Bob's farm investment limit:** `x <= 40000`
* **Non-negativity:** `x >= 0`, `y >= 0`


```python
import gurobipy as gp
from gurobipy import GRB

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

# Create variables
x = m.addVar(name="x", lb=0)  # Investment in Bob's farm
y = m.addVar(name="y", lb=0)  # Investment in Joe's farm

# Set objective function
m.setObjective(0.08 * x + 0.06 * y, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x + y <= 50000, "total_investment")
m.addConstr(x >= 3 * y, "investment_ratio")
m.addConstr(x <= 40000, "bobs_farm_limit")

# Optimize model
m.optimize()

# Print results
if m.status == GRB.OPTIMAL:
    print(f"Optimal investment in Bob's farm: ${x.x}")
    print(f"Optimal investment in Joe's farm: ${y.x}")
    print(f"Maximum earnings: ${m.objVal}")
elif m.status == GRB.INFEASIBLE:
    print("The model is infeasible.")
else:
    print(f"Optimization terminated with status {m.status}")

```
