To solve Jacob's investment problem, we first need to convert the natural language description into a symbolic representation. Let's denote:

- \(x_1\) as the amount invested in the logging industry,
- \(x_2\) as the amount invested in the shipping industry.

The objective is to maximize profit. Given that each dollar invested in logging yields $0.06 and each dollar in shipping yields $0.03, the objective function can be represented as:

\[ \text{Maximize:} \quad 0.06x_1 + 0.03x_2 \]

The constraints are:
1. A minimum of 50% of the money has to be invested in the logging industry: \( x_1 \geq 0.5(x_1 + x_2) \)
2. At least $1000 has to be invested in the shipping industry: \( x_2 \geq 1000 \)
3. The total investment cannot exceed $3000: \( x_1 + x_2 \leq 3000 \)
4. Non-negativity constraints: \( x_1 \geq 0, x_2 \geq 0 \)

In symbolic notation with natural language objects, we have:
```json
{
  'sym_variables': [('x1', 'amount invested in the logging industry'), ('x2', 'amount invested in the shipping industry')],
  'objective_function': '0.06*x1 + 0.03*x2',
  'constraints': ['x1 >= 0.5*(x1 + x2)', 'x2 >= 1000', 'x1 + x2 <= 3000', 'x1 >= 0', 'x2 >= 0']
}
```

Now, let's implement this problem in Gurobi using Python:

```python
from gurobipy import *

# Create a new model
m = Model("Investment_Problem")

# Create variables
x1 = m.addVar(vtype=GRB.CONTINUOUS, name="logging_investment")
x2 = m.addVar(vtype=GRB.CONTINUOUS, name="shipping_investment")

# Set the objective function
m.setObjective(0.06*x1 + 0.03*x2, GRB.MAXIMIZE)

# Add constraints
m.addConstr(x1 >= 0.5*(x1 + x2), "logging_minimum")
m.addConstr(x2 >= 1000, "shipping_minimum")
m.addConstr(x1 + x2 <= 3000, "total_investment")
m.addConstr(x1 >= 0, "non_negative_logging")
m.addConstr(x2 >= 0, "non_negative_shipping")

# Optimize model
m.optimize()

# Print solution
if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Logging Investment: {x1.x}")
    print(f"Shipping Investment: {x2.x}")
    print(f"Total Profit: {m.objVal}")
else:
    print("No optimal solution found")
```