## Step 1: Define the symbolic variables
Let's define the symbolic variables for the problem:
- $x_1$ represents the amount of money invested in Bob's farm.
- $x_2$ represents the amount of money invested in Joe's farm.

## Step 2: Translate the natural language description into a symbolic representation
The objective is to maximize earnings, where earnings from Bob's farm are $0.08x_1$ and from Joe's farm are $0.06x_2$. Thus, the objective function to maximize is $0.08x_1 + 0.06x_2$.

## 3: Define the constraints
The constraints based on the problem description are:
1. The total amount invested is at most $50,000: $x_1 + x_2 \leq 50000$.
2. The amount invested in Bob's farm is at least 3 times the amount invested in Joe's farm: $x_1 \geq 3x_2$.
3. The amount invested in Bob's farm can be at most $40,000: $x_1 \leq 40000$.
4. Non-negativity constraints: $x_1 \geq 0$ and $x_2 \geq 0$.

## 4: Symbolic representation of the problem
The symbolic representation is:
```json
{
'sym_variables': [('x1', 'Bob\'s farm investment'), ('x2', 'Joe\'s farm investment')],
'objective_function': '0.08*x1 + 0.06*x2',
'constraints': [
    'x1 + x2 <= 50000',
    'x1 >= 3*x2',
    'x1 <= 40000',
    'x1 >= 0',
    'x2 >= 0'
]
}
```

## 5: Convert the problem into Gurobi code
Now, let's convert this into Gurobi code in Python:
```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="Bob's_farm_investment", lb=0)
x2 = model.addVar(name="Joe's_farm_investment", lb=0)

# Objective function: maximize 0.08*x1 + 0.06*x2
model.setObjective(0.08*x1 + 0.06*x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 50000, name="Total_investment")
model.addConstr(x1 >= 3*x2, name="Bob_to_Joe_ratio")
model.addConstr(x1 <= 40000, name="Bob_investment_limit")

# Solve the model
model.optimize()

# Print the solution
if model.status == gp.GRB.OPTIMAL:
    print("Optimal investment in Bob's farm: $", x1.varValue)
    print("Optimal investment in Joe's farm: $", x2.varValue)
    print("Maximized earnings: $", 0.08*x1.varValue + 0.06*x2.varValue)
else:
    print("The model is infeasible")
```