## Symbolic Representation

To solve the given optimization problem, we first need to convert the natural language description into a symbolic representation.

Let's define the symbolic variables as follows:

- $x_1$ represents the amount invested in the vaccine industry.
- $x_2$ represents the amount invested in the meat-replacement industry.

The objective is to maximize the return on investment. The return from the vaccine industry is 5% of $x_1$, and the return from the meat-replacement industry is 7% of $x_2$. Therefore, the objective function can be represented as:

Maximize $0.05x_1 + 0.07x_2$

The constraints based on the problem description are:

1. Total investment is $100,000: $ $x_1 + x_2 \leq 100,000$
2. At least 60% of the investment should be in the meat-replacement industry: $x_2 \geq 0.6 \times 100,000$
3. At most 30% of the investment can be in the vaccine industry: $x_1 \leq 0.3 \times 100,000$
4. Non-negativity constraints: $x_1 \geq 0, x_2 \geq 0$

## Symbolic Representation in JSON Format

```json
{
    'sym_variables': [('x1', 'vaccine industry investment'), ('x2', 'meat-replacement industry investment')],
    'objective_function': '0.05*x1 + 0.07*x2',
    'constraints': [
        'x1 + x2 <= 100000',
        'x2 >= 0.6 * 100000',
        'x1 <= 0.3 * 100000',
        'x1 >= 0',
        'x2 >= 0'
    ]
}
```

## Gurobi Code in Python

```python
import gurobipy as gp

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

# Define the variables
x1 = model.addVar(name="vaccine_investment", lb=0)
x2 = model.addVar(name="meat_replacement_investment", lb=0)

# Objective function: Maximize 0.05*x1 + 0.07*x2
model.setObjective(0.05 * x1 + 0.07 * x2, gp.GRB.MAXIMIZE)

# Constraints
model.addConstr(x1 + x2 <= 100000, name="total_investment")
model.addConstr(x2 >= 0.6 * 100000, name="meat_replacement_min")
model.addConstr(x1 <= 0.3 * 100000, name="vaccine_max")

# Solve the model
model.optimize()

# Print the results
if model.status == gp.GRB.OPTIMAL:
    print(f"Optimal investment in vaccine industry: ${x1.varValue:.2f}")
    print(f"Optimal investment in meat-replacement industry: ${x2.varValue:.2f}")
    print(f"Maximal return: ${model.objVal:.2f}")
else:
    print("The model is infeasible.")
```